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ABSTRACT The upper reaches of the Yellow River, located at the northeastern edge of the Qinghai‐Tibet Plateau in China, are characterized by intense tectonic activity and widespread landslide geomorphology. Creating a detailed and objective map of landslide geomorphology in this region is crucial for understanding the development of landslides. However, the availability of high‐quality landslide inventories in this area is limited, hindering a comprehensive understanding of landslide development and scientific landslide disaster prevention efforts. This study utilized multi‐temporal high‐resolution remote sensing imagery provided by the Google Earth platform to conduct a thorough landslide geomorphology survey and inventory construction in Minhe County, located in the upper Yellow River. The results show that within the study area of 1890.82 km2, at least 5517 landslide geomorphologies were identified, covering an area of 434.43 km2, with landslide‐affected areas accounting for approximately 22.98% of the total area. The largest single landslide area is 1.62 × 106 m2, while the smallest single landslide area is 880.22 m2, with an average landslide area of 78743.04 m2. The highest landslide point density reached 11.5 km−2. More than 80% of the landslides were distributed in the townships of Qianhe, Zhongchuan, Guanting, and Bazhou. Landslides were predominantly distributed along the tributaries of the Huangshui and Yellow Rivers, with denser occurrences at river bends. In addition, some landslide geomorphologies are located in densely populated village areas, posing significant safety risks. These results provide valuable data support for further analysis of landslide spatial distribution in Minhe County, landslide risk assessment, and other related research. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks. However, the lack of interpretability in these models has raised concerns and hindered their widespread adoption in critical domains. Generating activation maps that highlight the regions contributing to the CNN's decision has emerged as a popular approach to visualize and interpret these models. Nevertheless, existing methods often produce activation maps contaminated with irrelevant background noise or incomplete object activation, limiting their effectiveness in providing meaningful explanations. To address this challenge, we propose Union Class Activation Mapping (UnionCAM), an innovative visual interpretation framework that generates high-quality class activation maps (CAMs) through a novel three-step approach. UnionCAM introduces a weighted fusion strategy that adaptively combines multiple CAMs to create more informative and comprehensive activation maps. First, the denoising module removes background noise from CAMs by using adaptive thresholding. Subsequently, the union module fuses the denoised CAMs with region-based CAMs using a weighted combination scheme to obtain more comprehensive and informative maps, which we refer to as fused CAMs. Lastly, the activation map selection module automatically selects the optimal CAM that offers the best interpretation from the pool of fused CAMs. Extensive experiments on ILSVRC2012 and VOC2007 datasets demonstrate UnionCAM's superior performance over state-of-the-art methods. It effectively suppresses background noise, captures complete object regions, and provides intuitive visual explanations. UnionCAM achieves significant improvements in insertion and deletion scores, outperforming the best baseline. UnionCAM makes notable contributions by introducing a novel denoising strategy, adaptive fusion of CAMs, and an automatic selection mechanism. It bridges the gap between CNN performance and interpretability, providing a valuable tool for understanding and trusting CNN-based systems. UnionCAM has the potential to foster responsible deployment of CNNs in real-world applications. [ABSTRACT FROM AUTHOR]

Researchers define gamification as the phenomenon of creating "gameful experiences" and the use of "game mechanics" in non‐gaming contexts (Deterding et al. 2011; Hamari et al. 2014). Gamification within education is the translation of design elements historically associated with gaming, e.g., embodiment, restructured timetables, probability, risk and reward, into the design of pedagogical approaches towards the goal of increasing student motivation, responsiveness and self‐determination. The following article examines the gamification of critiques and peer‐reviews as an evidence‐based best practice and disruptive innovation before outlining examples of critique games. The critique games in this article disrupt instinctive response frameworks and timetables and provide alternatives to more conventional critique and peer‐review practices. [ABSTRACT FROM AUTHOR]

The Qinling Mountains in China frequently experience geological disasters, with large‐scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km2. By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large‐scale landslides, covering a combined area of approximately 1575 km2. The average size of these landslides is approximately 92,734 m2. The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m2 and the largest reaching 12.9 km2. Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large‐scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area. [ABSTRACT FROM AUTHOR]

The Baota District of Yan'an City, located in the Loess Plateau, is an important patriotic education base in China. The region's fragile geological environment and frequent geological disasters pose significant threats to the production and livelihood of residents. Establishing a landslide traces inventory can provide crucial assistance for studying regional land disaster distribution patterns and implementing disaster prevention and mitigation measures. However, the Baota District has not yet established a comprehensive and detailed landslide traces inventory, resulting in a lack of clear understanding and comprehensive knowledge regarding the threats and impacts of landslide disasters in the area. Therefore, this study employed high-resolution satellite images, applying a human–computer interactive visual interpretation method in conjunction with field survey verifications, to develop the most detailed and comprehensive landslide traces inventory for the Baota District to date. The results indicate that within the 3556 km2 area of the Baota District, there are 73,324 landslide traces, with an average landslide density of 20.62 km-2 and a total landslide area of 769.12 km2, accounting for 21.63% of the total land area. These landslides are relatively evenly distributed throughout the district, with a higher concentration in the east compared to the west. Most of the landslides are small in size. This study can support disaster prevention and mitigation efforts in the Baota District and serve as a reference for establishing landslide inventories in other regions of the Loess Plateau. [ABSTRACT FROM AUTHOR]

The current ice lake dataset in the western region of Nyingchi requires further improvement. Due to the intricate distribution of ice lakes and imprecise boundary delineation, research tends to overlook small-scale ice lakes in this area. Moreover, most related studies have focused solely on variations in ice lake areas within key regions, such as the Himalayas, with little attention given to changes occurring in southeastern Tibet. The frequency of ice and snow disasters in the study area has been steadily increasing over the years. Therefore, this study utilizes Landsat satellite images and employs visual interpretation methods to generate more precise and comprehensive maps depicting the distribution of ice lakes in the western region of Nyingchi Province for the years 1994, 2010, 2018, and 2022. Additionally, changes in scale and spatial patterns of different types of ice lakes were investigated. Between 1994 and 2022, the ice lake area in the study area significantly increased by 22.5%, reaching a total of 35.8 ± 3.0 km2. This expansion was primarily driven by glacier-fed lakes, which experienced a remarkable growth rate of 30.8%. In contrast, the non-glacier-fed lakes experienced an increase by only 15.6%. Notably, ice lakes at higher elevations exhibited a peak in expansion, with those above 5143.0 m experiencing the most substantial growth rate of 44.8%. The long-term expansion rate of ice lakes is investigated through the measurement of changes in their boundaries, with the aim to understand the factors contributing to their growth. These findings indicate the rapid expansion of the ice lake near the glacier, with an annual growth rate of 1.3% per annum. Specifically, the glacial-fed section exhibited an expansion rate of 1.1% per annum, while the nonglacial-fed section experienced a growth rate of 0.6% per annum. The seasonal variability in marine glaciers is the primary factor influencing the expansion of ice lakes in this region, with temperature and precipitation serving as the principal driving forces impacting the transformation of these lakes. The data provided by the research results will facilitate a comprehensive understanding of the dynamics and mechanisms governing the ice lake in western Nyingchi, thereby contributing to an enhanced scientific comprehension of potential disaster risks associated with this ice lake.

Abstract The Qinling Mountains in China frequently experience geological disasters, with large‐scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km2. By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large‐scale landslides, covering a combined area of approximately 1575 km2. The average size of these landslides is approximately 92,734 m2. The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m2 and the largest reaching 12.9 km2. Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large‐scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area.

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains. [ABSTRACT FROM AUTHOR]

Cultivated land resources have restricted the agricultural production and economic development in China, particularly with the ever-increasing urbanization and population. There are the spatiotemporal variations of cultivated land in different regions, due to the different natural and social economic conditions. However, only a few quantitative studies have been focused on the distribution of cultivated land use under various terrain and climate in the field of geography. Furthermore, the data accuracy of most studies cannot accurately reflect the spatial change of cultivated land. Taking Ngari Prefecture as the research area, the purpose of this study was to explore the spatiotemporal differentiation and distribution of cultivated land resources under different terrain and climate. Farmland data was collected in 2005, 2013 and 2020. Google Earth highresolution imagery was then utilized to visually interpret with a resolution of 0.49-8.26 m. Kernel density estimation, spatial and buffer analysis functions in ArcGIS software were also used to identify the spatiotemporal differentiation and influencing factors of farmland. The results showed that: 1) In terms of spatial change, the scattered and fragmented cultivated land was more concentrated in the valleys of the southwestern part of Pulan County and the central part of Zada County. The small patches were dominated in the cultivated land. The cultivated land was also more concentrated in Pulan Township of Pulan County, Xiangzi Township of Zada County, Shiquanhe Town of Gar County and Ritu Town of Ritu County, compared with different townships. Meanwhile, the cultivated land was distributed mainly in the spatial area with an altitude of 3500-4500 m, a slope of 0-6°, a slope to the southwest, an average annual precipitation of 0-25 mm, an average annual temperature of 0-5°C, and a distance of less than 200 m from roads and rivers. 2) In terms of temporal change, the cultivated area was aggregated in a multiregional manner from 2005 to 2020, indicating a general trend of growth. The reason was attributed to the influence from the geographic location, natural environment, population increase, non-agriculturalization, and market demand. Specifically, the cultivated land area of Zada and Pulan counties in the south increased slowly or even declined, while there was the significant increase in the Gaer and Ritu counties in the north. 3) The cultivated land area was continued to expand in the future, in order to meet the demand of market and employment. There was the smaller increase in the south, together with the small decrease. An outstanding increase was found in the north. Therefore, the mechanization, the output value per unit area and the high-quality agriculture should be improved to focus on the environmental carrying capacity of regional arable land, soil quality and sustainability. In general, the farmland was also dominated by the location and hydrothermal conditions, particularly the influence from the population, non-agricultural development and market demand. The distribution and area changes of farmland were greatly varied in different regions. This finding can also provide a strong reference for the current status of land use, agricultural land planning and sustainable development of cultivated land in Ngari Prefecture. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems have garnered significant attention for their pivotal role in the global carbon cycle and their contributions to multiple UN Sustainable Development Goals. In China, the strategic pivot towards comprehensive ecological restoration in mangrove management underscores an urgent need for a nuanced understanding of mangrove evolutionary patterns. However, existing remote sensing-based information on mangrove dynamics exhibits discrepancies, lacking a cohesive and precise understanding. Accordingly, this study utilized high-resolution GaoFen imagery and precise visual interpretation methods to meticulously update an existing mangrove dataset through 2022, ensuring high accuracy and improved timeliness. Building on this, the study conducted a rigorous spatiotemporal analysis of mangrove dynamics from 1978 to 2022, developed an index to assess recovery status, and ultimately proposed targeted recommendations for conservation and restoration. Findings reveal that between 2018 and 2022, China’s mangrove expanse witnessed an average annual net increase of 2.82%, marking an unprecedented high. Breaking down the net change, the rate of gain accelerated, averaging 878.63 ha per year, but it is noteworthy that the losses also increased rapidly, tripling that of the previous period, mainly in regions with intense human activities such as the Pearl River Estuary. This finding complements the general perception of a continuous net increase in mangrove areas, highlighting the paradox of significant contraction occurring alongside rapid expansion. Additionally, by 2022, mangrove extent had recovered to 28,641 ha, essentially returning to the 1978 level, yet characterized by poor stability and significant spatial heterogeneity, with Guangdong and Hainan exhibiting recovery indices below the average. The study emphasizes that the ecosystem’s intrinsic restorative capabilities have emerged as a critical factor in offsetting losses and signaling a positive trend. The latest data provided by this study can offer actionable insights and support for mangrove conservation and restoration initiatives, serving the goal of ecosystem recovery and affirming their indispensable role in environmental governance.

Deep convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks. However, the lack of interpretability in these models has raised concerns and hindered their widespread adoption in critical domains. Generating activation maps that highlight the regions contributing to the CNN's decision has emerged as a popular approach to visualize and interpret these models. Nevertheless, existing methods often produce activation maps contaminated with irrelevant background noise or incomplete object activation, limiting their effectiveness in providing meaningful explanations. To address this challenge, we propose Union Class Activation Mapping (UnionCAM), an innovative visual interpretation framework that generates high-quality class activation maps (CAMs) through a novel three-step approach. UnionCAM introduces a weighted fusion strategy that adaptively combines multiple CAMs to create more informative and comprehensive activation maps. First, the denoising module removes background noise from CAMs by using adaptive thresholding. Subsequently, the union module fuses the denoised CAMs with region-based CAMs using a weighted combination scheme to obtain more comprehensive and informative maps, which we refer to as fused CAMs. Lastly, the activation map selection module automatically selects the optimal CAM that offers the best interpretation from the pool of fused CAMs. Extensive experiments on ILSVRC2012 and VOC2007 datasets demonstrate UnionCAM's superior performance over state-of-the-art methods. It effectively suppresses background noise, captures complete object regions, and provides intuitive visual explanations. UnionCAM achieves significant improvements in insertion and deletion scores, outperforming the best baseline. UnionCAM makes notable contributions by introducing a novel denoising strategy, adaptive fusion of CAMs, and an automatic selection mechanism. It bridges the gap between CNN performance and interpretability, providing a valuable tool for understanding and trusting CNN-based systems. UnionCAM has the potential to foster responsible deployment of CNNs in real-world applications.

With advances in satellite technology, Polarimetric Synthetic Aperture Radar (PolSAR) now have higher resolution and better data quality, providing excellent data conditions for the refined visual interpretation of artificial targets. The primary method currently used is a multicomponent decomposition, but this method can result in pixel misdivision problems. Thus, we propose a non-fixed threshold division method for achieving advanced feature ship structure characterization in full-polarimetric SAR images. Yamaguchi decomposition can effectively identify the primary scattering mechanism and characterize artificial targets. Its modified volume scattering model is more consistent with actual data. The polarization entropy can serve as the target scattering mechanism at a specified equivalent point in the weakly depolarized state, which can effectively highlight the ship structure. This paper combines the three components of the Yamaguchi decomposition algorithm with the entropy, and divides it into a nine-classification plane with a non-fixed threshold. This method reduces category randomness generated by noise at the threshold boundary for complicated threshold treatments. Furthermore, the Mixed Scattering Mechanism (MSM) which is the region where both secondary scattering and single scattering are significant, was proposed to better match the scattering types of typical structures of vessels in the experiment. The Generalized Similarity Parameter (GSP) was used to further shorten the intra-class distance and perform iterative clustering using a modified GSP-Wishart classifier. This method improves the vessel distinguishability by enhancing the secondary and mixed scattering mechanisms. Finally, this paper uses full-polarimetric SAR data from a port in Shanghai, China, for the experiment. We collected and filtered ship information and optical data from this port through the Automatic Identification System (AIS) and matched them with the ships in full-polarimetric SAR images to verify the correct characterization of each vessel’s features. The experimental results show that the proposed method can effectively distinguish three types of vessels: bulk carriers, container ships and tankers.

Reinforcement learning can achieve autonomous learning in dynamic and complex environments, which makes it widely used in fields such as law, medicine, and finance. However, reinforcement learning still faces many problems such as the unobservable global state space, strong dependence on the reward function, and uncertain causality, which results in its weak interpretability, seriously affecting its promotion in related fields. It will encounter limitations such as difficulty in judging whether the decision-making violates social legal and moral requirements, whether it is accurate and trustworthy, etc. In order to further understand the current status of interpretability research in reinforcement learning, this article discussed from the aspects of interpretable models, interpretable strategies, environment interaction and visualization, etc. Based on these, this article systematically discussed the research status of reinforcement learning interpretability, classified and explained its explainable methods, and finally proposed the future development direction of reinforcement learning interpretability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The upper reaches of the Yellow River in China, influenced by erosion of the Yellow River and tectonic activities, are prone to landslides. Therefore, it is necessary to investigate the existing landslide traces. Based on visual interpretation on high-resolution satellite images and terrain data, supplemented and validated by existing landslide records, this paper prepared the most complete and detailed landslide traces inventory in Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, to date. The results indicate that within the study area of 1714 km², there are at least 713 landslide traces, ranging in scale from 3,556 m2 to 11.13 km², with a total area of 134.46 km². The total landslide area excluding the overlap area is 126.30 km². The overall landslide point density and area density in the study area are 0.42 km[sup -2] and 7.37% respectively. The maximum point density and maximum area density of landslide traces in the area are as high as 5.69 km-2 and 98.0% respectively. The landslides are primarily distributed in the relatively low-elevation northeastern part of Jianzha County, characterized mainly by large-scale loess landslides, with 14 landslides exceeding 1x10[sup 6] m². This inventory not only supplements the landslide trace data in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, but also provides an important basis for subsequent landslide risk zoning, response to climate change, and landscape evolution. Additionally, it holds significant reference value for compiling landslide inventories in similar geological environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The complex geological environment and strong tectonic movement have led to the development of a large number of ancient landslides along the Jinsha River. These landslides exhibit characteristics of large-scale, complex formation mechanisms, multiple sliding periods, and high potential hazards. In this study, we aim to construct an ancient landslide inventory and conduct potential landslide hazard assessment of the Wudongde hydropower station section and its surrounding areas, which is located in the downstream area of Jinsha River. We used the visual interpretation method to recognize large ancient landslides based on high-resolution remote sensing images on the Google Earth platform and analyzed the correlations between the landslide abundance and different influencing factors. Our results show that there were 3126 ancient landslides in the study area, covering a total area of 502.64 km2. The statistical analysis indicated that the landslide occurrence is closely related to the slope gradient and topographic relief, and the landslide abundance index increases with the increase in above two influencing factors. In addition, the ancient landslides gradually decreases with the increase in the elevation, indicating that ancient landslides are more likely to occur in lower elevation areas, i.e., lower portion of the hillslopes. In addition, combining with machine learning method (logistic regression), the potential landslide hazard assessment of the study area was calculated by the hypothetical earthquake scenario of 10% exceedance probability in 50 years. The predicted result shows that the extremely high-hazard area of landslides appeared around the hydropower station, and the high-hazard area was mainly distributed within a 5-km range along both banks of the Jinsha River. This study provides basic data and important reference for the distribution characteristics and potential hazard assessment of ancient landslides in the reservoir area of Wudongde hydropower station. [ABSTRACT FROM AUTHOR]

Earthquakes are hard to predict, and the destruction caused by the events far outstrip the monetary damage. Important cultural heritage sites functioning as places of community and identity have a value which evades pure pecuniary calculation. This makes understanding the complete economic and social impact of earthquakes a difficult and daunting task. We use high-resolution TerraSAR-X data acquired after the 2023 earthquake in Turkey to assess its impact on selected cultural heritage sites. Leveraging different orbit and incidence angles of image acquisition allow us to show the difficulties in interpreting high-resolution SAR data. While large impacts, like the complete collapse of structures, can be detected successfully, small-scale damage and partial collapses are often difficult to detect from single SAR images. We find that single SAR scene interpretation for damage assessment of cultural heritage is not a viable option. While contextualizing data might help to understand the situation, SAR is only helpful if data of the intact cultural heritage sites have been obtained before the event.

The Baota District of Yan’an City, located in the Loess Plateau, is an important patriotic education base in China. The region’s fragile geological environment and frequent geological disasters pose significant threats to the production and livelihood of residents. Establishing a landslide traces inventory can provide crucial assistance for studying regional land disaster distribution patterns and implementing disaster prevention and mitigation measures. However, the Baota District has not yet established a comprehensive and detailed landslide traces inventory, resulting in a lack of clear understanding and comprehensive knowledge regarding the threats and impacts of landslide disasters in the area. Therefore, this study employed high-resolution satellite images, applying a human–computer interactive visual interpretation method in conjunction with field survey verifications, to develop the most detailed and comprehensive landslide traces inventory for the Baota District to date. The results indicate that within the 3556 km2 area of the Baota District, there are 73,324 landslide traces, with an average landslide density of 20.62 km-2 and a total landslide area of 769.12 km2, accounting for 21.63% of the total land area. These landslides are relatively evenly distributed throughout the district, with a higher concentration in the east compared to the west. Most of the landslides are small in size. This study can support disaster prevention and mitigation efforts in the Baota District and serve as a reference for establishing landslide inventories in other regions of the Loess Plateau.

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains.

In recent years, deep neural networks have shown superior performance in various fields, but interpretability has always been the Achilles' heel of deep neural networks. The existing visual interpretation methods for deep neural networks still suffer from inaccurate and insufficient target localization and low-resolution saliency maps. To address the above issues, this paper presents a saliency map generation method based on image geometry augmentation and super-resolution called augmented high-order gradient weighting class activation mapping (augmented grad-CAM++). Unlike previous approaches that rely on a single input image to generate saliency maps, this method first introduces the image geometry augmentation technique to create a set of augmented images for the input image and generate activation mappings separately. Secondly, the augmented activation mappings are combined to form the final saliency map. Finally, a super-resolution technique is introduced to add pixel points to reconstruct the saliency map pixels to improve the resolution of the saliency map. The proposed method is applied to analyze standard image data and industrial surface defect images. The results indicate that, in experiments conducted on standard image data, the proposed method achieved a 3.1% improvement in the accuracy of capturing target objects compared to traditional methods. Furthermore, the resolution of saliency maps was three times higher than that of traditional methods. In the application of industrial surface defect detection, the proposed method demonstrated an 11.6% enhancement in the accuracy of capturing target objects, concurrently reducing the false positive rate. The presented approach enables more accurate and comprehensive capture of target objects with higher resolution, thereby enhancing the visual interpretability of deep neural networks. This improvement contributes to the greater interpretability of deep learning models in industrial applications, offering substantial performance gains for the practical deployment of deep learning networks in the industrial domain. [ABSTRACT FROM AUTHOR]

Earthquakes are hard to predict, and the destruction caused by the events far outstrip the monetary damage. Important cultural heritage sites functioning as places of community and identity have a value which evades pure pecuniary calculation. This makes understanding the complete economic and social impact of earthquakes a difficult and daunting task. We use high-resolution TerraSAR-X data acquired after the 2023 earthquake in Turkey to assess its impact on selected cultural heritage sites. Leveraging different orbit and incidence angles of image acquisition allow us to show the difficulties in interpreting high-resolution SAR data. While large impacts, like the complete collapse of structures, can be detected successfully, small-scale damage and partial collapses are often difficult to detect from single SAR images. We find that single SAR scene interpretation for damage assessment of cultural heritage is not a viable option. While contextualizing data might help to understand the situation, SAR is only helpful if data of the intact cultural heritage sites have been obtained before the event. [ABSTRACT FROM AUTHOR]

In line with the study of land use and land cover, remote sensing technology has been welcomed by many researchers as a source of spatial information production and suitable tools, which shows the accuracy and validity of these maps. The purpose of this research is to evaluate and compare the accuracy of preparing land use maps using two methods of remote sensing and one method of visual interpretation of Google Earth images in the Kasilian representative watershed. In this research, after taking educational samples using Google Earth software and implementing them on the Landsat 9 image of 2021, classification of images was done in ENVI software, and the land use map was prepared based on training samples, Neural Network and SVM methods. In the method of visual interpretation, all land uses in Google Earth images were manually digitized and a land use map was obtained. Then, the accuracy of the map was checked for all three methods and the results showed that the map obtained from visual interpretation of Google Earth images with overall accuracy and Kappa coefficient of 100% was in agreement with the ground reality compared to Neural Network and SVM methods with overall accuracies of 87.6% and 88.2% and Kappa coefficients of 76% and 77.8%, respectively. However, due to the time-consuming visual interpretation method, especially for large watersheds, and the acceptable accuracy of Neural Network and SVM methods, it is suggested to use advanced methods to prepare land use maps, especially in large watershed [ABSTRACT FROM AUTHOR]

The traditional medium-resolution satellite images cannot meet the need of small-scale shoreline variation monitoring. In addition, researches on the influences in spatial resolution on monitoring different types of shoreline changes are rarely found and discussed. Hence, in this study, based on high resolution remote sensing images from 2010 to 2020, such as SPOT5, GF-1/6 and ZY-3 satellite images the characteristics of shoreline changes were researched by the method of digital shoreline analysis system (DSAS) and fractal dimension. Moreover, the influences on shoreline variation velocity and fractal dimension based on satellite images with different spatial resolution were analyzed corresponding Landsat images. The outputs showed that the fractal dimensions of different types of shorelines were similar based on high-and low-resolution images, yet the difference of spatial resolution had obvious influence on shoreline variation velocities of different types of shorelines. Additionally, the trend of shoreline spatio-temporal variation transitioned from drastic change to a relatively stable state. The results could provide data support for the optimization of resource utilization in the Bohai Bay area and the protection of natural resources such as coastlines and tidal flats and wetlands.

Which references are considered necessary for understanding and empathy in visual interpretation of translated feature films? This is the starting point for this article on audiovisual translation and visual interpretation. Visual interpretation is a scientifically relatively unexplored field of research that can be linked to both cognitive science, semiotics, and audiovisual translation. Just over a decade ago, there was little or no research into visual interpretation in Sweden or the Nordic countries. The first Swedish research initiatives started in the form of workshops in sight interpretation organized by Jana Holsanova, Mats Andrén and Cecilia Wadensjö (2010-2014) and resulted in a report on sight interpretation (Holsanova et al. 2016). The task of the visual interpreter is to select and describe relevant information such as events, environments, people, characters and their appearance, facial expressions, gestures, and body movements in television programs, cinema, or theater performances by giving verbal descriptions of visual scenes to evoke vivid mental images and audience empathy. Visual interpretation should contribute to our understanding and convey impressions and mood. It is a so-called intermodal translation, because the visual interpreter transfers content from image to words (Jakobson 1959; Reviers 2017). Through the language, those who listen should be able to follow along in the action. But they should not only know what is happening, but also be able to laugh at the same time as everyone else, understand why a certain sound occurs when it is heard and know who is doing what. It is thus about a completion of what is missing in the multimodal interaction (Holsanova 2020: 4). According to professional visual interpreters, the aim is to use a neutral voice to be clear, concise, and descriptive, so that the target group can imagine what something looks like with the help of internal images. In today’s rapid technological development, we also want to reflect on the opportunities and challenges of automated visual interpretation and translation, using ChatGPT.

AbstractIn this study, the region along the Mongolia-China-Russia Economic Corridor was selected as the subject. Here, a freeze-thaw erosion model would be built and used. By contrasting the probability of a geological disaster under the effect of an influencing factor with the probability of a geological disaster in the whole research region, the information value model is realized. In order to identify the most prone factors of freeze-thaw erosion in the research region, we replaced the possibility of geological disasters with the possibility of freeze-thaw erosion. The study region under each influencing factor was separated into five grades. Each grade’s information value was computed using the principles of the information quantity model and the freezing and thawing erosion grade division. Since there were positive and negative information values, for the purposes of calculating probabilities, they were normalized to obtain the similar information value for each grade before calculating the overall similar information value of each factor. The similar information value model (SIVM) underlies this procedure determines the weight. We compared the Analytic Hierarchy Process (AHP) and SIVM with the results of 0.070–0.758 and 0.063–0.776, respectively. Finally, the outcomes were confirmed through visual interpretation.

Supporting successful global mangrove conservation and policy requires accurate identification of anthropogenic and biophysical drivers of mangrove extent, yet such studies are scarce. We apply a hybrid methodology, combining existing remote sensing mangrove maps with local expert knowledge of vegetation and land use dynamics. We conducted stratified random sampling in eight subregions, and local experts visually interpreted over 20,900 plots using high-resolution imagery in Collect Earth Online. Similar to previous estimates, we found 147,771 km2 (±1.4%) of mangroves globally in 2020 and that rates of mangrove loss have decreased from 2000–2010 to 2010–2020, largely driven by South and Southeast Asia. Anthropogenic drivers of loss have shifted across subregions, with oil palm cultivation emerging in South and Southeast Asia and aquaculture in South America and Western and Central Africa, highlighting the need for ongoing monitoring and adaptable conservation efforts. Natural expansion outpaced natural retraction in both periods. This is the first global study uncovering land use drivers of mangrove decline and recovery, only made possible by collaboration with local experts. Key breakthroughs include successfully discerning spectrally similar anthropogenic from biophysical drivers, such as aquaculture from natural retraction, and creating data collection approaches that streamline visual interpretation efforts. [ABSTRACT FROM AUTHOR]

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Walking detection in the daily life of patients with Parkinson’s disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.

The June 10, 2022 Ms5.8, Ms6.0 and Ms5.2 earthquake swarm of Maerkang, Sichuan, China has triggered a lot of landslides. The primary purpose of this paper is to prepare a detailed coseismic landslides inventory and ancient landslide inventory in the earthquake swarm-affected area. Based on pre- and post-quake high-resolution optical satellite images (planet), we delineated 650 individual coseismic landslides in the VI-intensity area and above, occupying an area of about 1.2 ​km2. The largest landslide covers about 70217 ​m2 in horizontal projection, while the smallest one is just 81 ​m2. In addition, based on the series of high-precise images on the Google Earth Platform, 759 ancient landslides were also delineated with an area of 117 ​km2. The maximum area reaches about 2 ​km2 and the minimum is 2580 ​m2. The two inventories provide a basis for landslide spatial distribution and hazard assessment, disaster prevention and mitigation of earthquake-triggered landslides in similar regions. We opened these two landslide inventories to facilitate researchers to carry out more in-depth work.

In this study, we obtained a regional landslide database of Jiyuan City based on the high-resolution remote sensing images of Google Earth platform. As a result, 605 landslides were identified, with a total area of 24.53 ​km2. Based on the GIS platform, the landslide number density (LND) and landslide area percentage (LAP) were used to statistically analyze the spatial distribution of landslides in the study area. Elevation, slope, aspect, curvature, drainage and stratum were selected as the influencing factors. The results show that the elevation interval where landslides are most likely to occur is 399–650 m. The slope most prone to landsliding is 44.6°–70.6°. The landslides with high frequency appear on the slope facing northwest. Slopes with curvature between −1–0 and 0–1 have relatively high landslide frequency, but no indication that whether on concave or convex. The water system in the entire study area has less impact on landslides. Landslides are most likely to occur in Neogene and Paleogene (N ​+ ​E) strata, while the number of landslides in the Permian (P) stratum is the highest, which means a high incidence. The results can provide a scientific basis for landslide hazard prevention and mitigation in the study area, and also for other regions.

Ascaridia galli is an important nematode that causes ascaridiasis in free-range and indoor system chicken farms. Infection with A. galli may damage the intestinal mucosa and inhibit nutrient absorption, leading to a reduced growth rate, weight loss and a decreased egg production. Consequently, A. galli infection is a significant health problem in chickens. In this study, we developed a loop-mediated isothermal amplification coupled with a lateral flow dipstick (LAMP-LFD) assay for the visual detection of A. galli eggs in faecal samples. The LAMP-LFD assay consists of six primers and one DNA probe that recognize the internal transcribed spacer 2 (ITS2) region; it can be performed within 70 min and the results can be interpreted with the naked eye. Using the LAMP-LFD assay developed in this study, A. galli DNA was specifically amplified without any cross-reactions with other related parasites (Heterakis gallinarum, Raillietina echinobothrida, R. tetragona, R. cesticillus, Cotugnia sp., Echinostoma miyagawai) and definitive hosts (Gallus gallus domesticus, Anas platyrhynchos domesticus). The minimum detectable DNA concentration was 5 pg/μl, and the detectable egg count was 50 eggs per reaction. The assay can be performed in a water bath, without the need for post-mortem morphological investigations and laboratory instruments. It is therefore a viable alternative for the detection of A. galli in chicken faeces and can replace classical methods in field screening for epidemiological investigations, veterinary health and poultry farming management. This is the first study using the LAMP-LFD assay for Ascaridia galli detection. The results can be observed by the naked eye. The developed assay can be used to detect Ascaridia galli eggs in faecal samples. [ABSTRACT FROM AUTHOR]

This article offers an account of all illustrated editions of William Falconer's The Shipwreck (1762), including anthologies such as John Roach's Beauties, that were produced up to the end of the handpress period. It examines both the illustrations of specific scenes or moments from the poem and other visual paratext. In addition to providing a detailed study of the editions, including their marketing and pricing, it focuses on interpretive shifts in the illustrations—from exclusively ship- and shipwreck-related iconography, to the expression of human concerns, especially in terms of the tragic-sentimental mode that characterizes a large number of these illustrations. The illustrations will not be discussed in isolation but will be related to how artists at various exhibitions in that period engaged with Falconer's work and made present various aspects of it. In its comprehensive coverage of how publishers catered to different groups of buyers, the essay recovers a hitherto uncharted visual archive stemming from Falconer's poem. Questions related to readership, visual literacy, the commodification of the visual images accompanying The Shipwreck, and the role illustrations played in the mediation of the work will underpin my account of how the visual apparatuses added to editions affected the marketing and reception of Falconer's poem. [ABSTRACT FROM AUTHOR]

The largest tropical peatland complex in the Cuvette Centrale is marked by persistent knowledge gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in Cuvette Centrale, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo (DRC). Utilizing peatland maps and Radar for Detecting Deforestation alert data, we analyzed spatial and temporal patterns of disturbances. Further, we examined 2267 randomly sampled peat forest disturbance events through visual interpretation of monthly Planet and Sentinel 2A data to identify direct drivers. Our findings revealed that between 2019 and 2021, about 91% of disturbances occurred in DRC, with hotspots concentrated in the northwest Sud-Ubangi district. Disturbances predominantly followed a sharp seasonal pattern, recurring during the first half of each year with temporal hotspots emerging between February and May, closely associated with smallholder agriculture activities. Smallholder agriculture accounted for over 88% of disturbances in Cuvette Centrale, representing a leading role both in ROC (∼77%) and DRC (∼89%). While small-scale logging contributed 7% to the disturbances in the region, it constituted an important driver (18%) in the ROC. Other drivers included floods, roads, and settlements. Approximately 77% of disturbances occurred outside managed forest concessions in Cuvette Centrale, with 40% extending into protected areas. About 90% of disturbances were concentrated within 1 km of peat forest edges and ∼76% of the disturbances occurred within 5 km of road or river networks. The insights underscore the crucial need for effective peat forest conservation strategies in Cuvette Centrale and can inform national policies targeting peatland protection, aligning with commitments in the Brazzaville Declaration and the Paris Agreement. Further, our findings on direct driver assessment could serve as a reference dataset for machine learning models to automate the visual interpretation and upscale the assessment across the entire region.

Southwest China is an area where geological hazards occur frequently. To develop a basic understanding of geological hazards in this region, the Baihetan hydropower station section of the Jinsha River basin and its surrounding region are selected as the study area. We compile a detailed inventory including the information of all large-scale landslides (i.e., >104 ​m2 in area) and analyze their typical characteristics. In the study region that covers an area of 6372.9 ​km2, we identify 3757 large-scale landslides with a total area of 1033.5 ​km2 and an average area of 275,097 ​m2 for each individual landslide. Specifically, the Qiaojia mega-landslide has the largest area, reaching 7,717,000 ​m2. These landslides are clustered and unevenly distributed, and those along rivers are characterized by zonal distribution. The analysis of the influencing factors of landslide occurrence shows that elevation, river valley (valley), fault, slope angle, and slope aspect have obvious influences on landslide occurrence. This study is not only beneficial for further understanding the development characteristics of landslides in mountainous areas of southwest China, but also provides basic data for subsequent landslide susceptibility mapping in this region.

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100 % . We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application. [ABSTRACT FROM AUTHOR]

Purpose: Esophageal high-resolution manometry (HRM) revolutionized esophageal function testing due to the intuitive colorful and agreeable-to-the-eyes plots (Clouse plots). HRM execution and interpretation is guided by the Chicago Classification. The well-established metrics for interpretation allows a reliable automatic software analysis. Analysis based on these mathematical parameters, however, ignores the valuable visual interpretation unique to human eyes and based on expertise. Methods: We compiled some situations where visual interpretation added useful information for HRM interpretation. Results: Visual interpretation may be useful in cases of hypomotility, premature waves, artifacts, segmental abnormalities of peristalsis, and extra-luminal non-contractile findings. Conclusion: These extra findings can be reported apart from the conventional parameters. [ABSTRACT FROM AUTHOR]

The article addresses a project of visualization of research on ambient music, including the historically changing subject of this research, its theoretical background and qualitative studies arising from it. In this study, the visualization of the research process is related to the concept of graphesis, or visual interpretation, discussed and partly problematized in the context of visual representation of interdisciplinary topics at the interface of various knowledge systems. [ABSTRACT FROM AUTHOR]

Landslides are one of the most destructive geological disasters that threaten people's lives and property. With the high-resolution satellite images from Google Earth and the method of visual interpretation, we develop a large-scale landslide inventory in Lvliang City in the east of the Loess Plateau. The inventory includes a total of 12,110 landslides, and the area of each individual landslide ranges from 0.03 to 1.35 ​km2. The landslide area ratio (LAR) is used to measure the influence of several geological factors on landslides, including stratigraphic chronology, elevation and slope. From Paleozoic to Cenozoic, LAR generally shows an increasing trend. With the increase of elevation, LAR initially increases and then decreases, reaching the peak value in the elevation range of 1000–1100 ​m. With the increase of slope angle, LAR also increases first and subsequently decreases, and reaches the peak value in the range of 15–20°. In addition, we analyze the landslide mobility level (H/L) of 12,110 landslides in the study area, and observe a linear relationship between landslide height and sliding distance. Landslides with larger areas exhibit high mobility, manifested as the fact that H/L decreases with the increase of the landslide area. Meanwhile, both slope angle and aspect exert obvious effects on the mobility of landslides.

[Display omitted] • A high-precision, up-to-date dataset of China's mangroves was established. • Rapid mangrove gain paralleled with increasing losses from 2018 to 2022. • Mangrove area returned to 1978 level by 2022, with instability and spatial disparity. • Mangroves' restoration ability has become a key factor in offsetting losses. Mangrove ecosystems have garnered significant attention for their pivotal role in the global carbon cycle and their contributions to multiple UN Sustainable Development Goals. In China, the strategic pivot towards comprehensive ecological restoration in mangrove management underscores an urgent need for a nuanced understanding of mangrove evolutionary patterns. However, existing remote sensing-based information on mangrove dynamics exhibits discrepancies, lacking a cohesive and precise understanding. Accordingly, this study utilized high-resolution GaoFen imagery and precise visual interpretation methods to meticulously update an existing mangrove dataset through 2022, ensuring high accuracy and improved timeliness. Building on this, the study conducted a rigorous spatiotemporal analysis of mangrove dynamics from 1978 to 2022, developed an index to assess recovery status, and ultimately proposed targeted recommendations for conservation and restoration. Findings reveal that between 2018 and 2022, China's mangrove expanse witnessed an average annual net increase of 2.82%, marking an unprecedented high. Breaking down the net change, the rate of gain accelerated, averaging 878.63 ha per year, but it is noteworthy that the losses also increased rapidly, tripling that of the previous period, mainly in regions with intense human activities such as the Pearl River Estuary. This finding complements the general perception of a continuous net increase in mangrove areas, highlighting the paradox of significant contraction occurring alongside rapid expansion. Additionally, by 2022, mangrove extent had recovered to 28,641 ha, essentially returning to the 1978 level, yet characterized by poor stability and significant spatial heterogeneity, with Guangdong and Hainan exhibiting recovery indices below the average. The study emphasizes that the ecosystem's intrinsic restorative capabilities have emerged as a critical factor in offsetting losses and signaling a positive trend. The latest data provided by this study can offer actionable insights and support for mangrove conservation and restoration initiatives, serving the goal of ecosystem recovery and affirming their indispensable role in environmental governance. [ABSTRACT FROM AUTHOR]

Community forestry is a regime of forest management that engages local communities to conserve forests and improve their livelihoods. As the number of community-conserved forests grows, a growing body of evidence indicates the positive effects of community forests in reducing deforestation. However, there is little analysis encompassing the comprehensive effectiveness of community forests (CFs) in terms of deforestation, forest degradation, forest cover change and forest increase. Here, we conducted a comprehensive analysis to investigate the influence of CFs on these aspects between 2015 and 2019 in two watershed conservation forests in Myanmar. We used visual interpretation of very high-resolution satellite imagery and applied propensity score matching to ensure a balanced distribution of covariates. When compared directly, deforestation inside CFs (5.08 %) were higher than those outside CFs (3.89 %), while forest degradation (23.73 %) and forest increase (11.86 %) inside CFs were lower than those outside CFs (24.9 % and 16.34 %, respectively). However, these differences were not significant, and the matching results showed that CFs did not exhibit significant control over deforestation, forest degradation, forest cover change, and improvements in forest cover compared to areas outside CFs. We conclude that establishing community forests alone does not guarantee forest conservation in the short term. Therefore, community-based forest management practices are needed to address deforestation and forest degradation and achieve effective forest conservation aligned with local needs. • Effective management of community forests (CFs) in the short term is challenging. • CFs have shown no significant effect on forest conservation. • Visual interpretation enhances the assessment of CFs' role in forest conservation. • Management practices aligned with local needs may improve forest conservation. [ABSTRACT FROM AUTHOR]