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The Cauvery Basin is one of the prolific hydrocarbon-producing basins of southern India. The pericratonic basin has five sub-basins separated by five basement and structural highs formed by granitic and gneissic rocks of the Archaean Southern Granulite Terrane. The sub-basins have excellent Cretaceous source rocks and hydrocarbon-bearing reservoirs of different geological ages. Commercial hydrocarbons are produced from the Archean basement highs, specifically from the Kumbakonam–Madanam palaeo-highs of the Cauvery basin. The paper addresses basement characterisation using conventional as well as advanced well logs for accurate characterisation of the basement reservoirs. Basement reservoirs are challenging in terms of sporadic porosity and permeability distribution. Most of the porosities and permeability are attributed to secondary generation by fracturing and weathering. The present work aims to understand and compare the petrophysical attributes of two kinds of basement reservoirs (fractured basement and weathered basement) on the Kumbakonam–Madanam High. Two wells from each type lying on either side of the Madanam High were selected. Well log data, including gamma-ray, resistivity, porosity (neutron and bulk density), acoustic logs and advanced logs, such as resistivity images and dipole acoustic and elemental capture spectroscopy logs, from these four wells were analysed for their petrophysical understanding. Furthermore, image and acoustic log data were integrated to characterise the fracture geometry and fracture permeability of the basement reservoirs. Conventional log suites showing low GR, low resistivity, high density, and high neutron porosity are indicative of intricate lithologies, possibly mafic rocks. A notable negative crossover in density and neutron log along with excess Si concentration indicates weathering. A higher fracture density with a crisscross fracture/mesh fracture pattern is indicated by the analysis of image logs, shear wave anisotropy, and Stoneley fractures. Thus, a comparison of the petrophysical attributes of both fields is attempted to understand the fractured and weathered basement reservoirs and their geological characteristics. In conclusion, the basement reservoirs of the Madanam High Field are of interest because of their hydrocarbon-producing ability, and proper synthesis of petrophysical attributes will help develop activities in the basement reservoirs. [ABSTRACT FROM AUTHOR]

This article examines the effects of WhatsApp as a mode of dissemination of political posters. It found that platform affordances that control the crafting and dissemination of political messages open up the possibility of vague political messaging by conforming to the social media's visual culture and limit the spread of these messages, restricting the ability to organically gather support for a political cause. Despite the growing appeal of social media in political campaigns, social media messages when used by individuals and small, independent social media groups, who are not a part of a larger, organized political party or movement, have little influence on electoral decisions of voters about a political cause that faces weak public support. This was discussed in the context of electoral results of the Leftist political party in India in 2019 national elections. The paper then contributes to our understanding of the extent of the influence of social media platforms on political media messages. [ABSTRACT FROM AUTHOR]

Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram images that are highly sensitive to noise. Subsequently, segmentation is done using modified adaptively regularized kernel-based fuzzy C means (ARKFCM). Feature extraction is accomplished using the morphological, texture, and moment invariants. The corresponding feature values are provided as inputs to a deep neural network (DNN) model that classifies the normal and abnormal portions in the mammogram images. The performance of the proposed model is validated with the Mammographic Image Analysis Society (MIAS) database. The efficiency of the proposed classifier is experimentally proved by comparing the various classifiers with respect to their statistical performances. On an applied database, the proposed model offered a maximum classification with the highest accuracy level of 99.13%. [ABSTRACT FROM AUTHOR]

Land use/Land cover (LULC) plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem. This study mainly focuses on the usage of the geographic information system (GIS) and land usage to identify the changes in Ananthapuramu, which is located in the Ananthapur district of Andhra Pradesh (AP) state, South India. This paper mainly focuses on the classification and identification of the changes in LULC in the period of 1999–2019 based on a survey of India's topographic map and temporal images gathered from the satellite. The experimental results indicate that over the specified period of study, the rate of increment and decrement in the built-up area, rate of underwater bodies, forest bodies that may demonstrate a significant impact on the environmental ecosystem. [ABSTRACT FROM AUTHOR]

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. [ABSTRACT FROM AUTHOR]

To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach. [ABSTRACT FROM AUTHOR]

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We have installed a multi-wavelength allsky airglow imager over the Indian Himalayan region at Hanle, Leh Ladakh, (32.7°N, 78.9°E; dip lat. ∼24.1°N). This is the first of its kind that is installed in the Himalayan region at an altitude of around 4200 m above the mean sea level. The high sensitive thermoelectrically cooled CCD based imager is equipped with two interference filters (557.7 nm and 630.0 nm with a bandwidth of 2 nm). The airglow imaging observations have been carried out during moonless nights since June 2018. This paper presents the detailed steps of the image processing techniques to transform the raw images into the geospatially-calibrated images and discusses the process for the estimation of gravity wave parameters (horizontal wavelength, apparent phase velocity and periodicities) from the processed images. In addition, we report a few interesting events like Ripple-type structures, gravity waves and mesospheric bores in the mesopshere and lower thermosphere (MLT) region and the plasma disturbances in the ionospheric F region observed for the first time over Hanle, Leh Ladakh. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for tile image reconstruction. This methodology is integrated in a computer tool that allows automatic image analysis. Both, the reconstruction and analysis techniques are based on vectorial tools, the application of the theory of symmetry groups and perceptual psychology. Image analysis allows us to obtain information about design patterns which is used to restitute (recover missing motifs), unify (choose between different motifs that should be equal) and standardize (fit motifs exactly to their symmetry axes and rotation centers) old designs. This methodology for tile image reconstruction has been applied to different Islamic mosaics from the Alhambra of Granada (Spain), the Aq-Saray Palace of Shar-i-Sabz (Uzbekistan) and the Red Fortress of Agra (India). The analysis results are presented in this paper. [ABSTRACT FROM AUTHOR]

The tectonic evolution of the Sub-Himalayan longitudinal intermontane valleys, called 'Duns', in the Kumaun region of India is not yet understood as the thick forest cover and extensive anthropogenic modifications of the landscape have made it difficult to identify the various tectonic and geomorphic features. In this paper we present a conceptual model of the tectonic evolution of Kota and Pawalgarh Duns of the region, based on interpretation of digitally processed IRS P6 LISS III imagery and analyses of Digital Terrain Models (DTMs) in a Geographic Information System (GIS), together with field investigations. The Duns were formed as the fault-bend folded hills came up in front of the southern piedmont zone of the frontal Himalaya along the Himalayan Frontal Thrust (HFT) and the Pawalgarh Fault. The landscape of the Duns is further modified as a result of movement along subsequently developed transverse faults. [ABSTRACT FROM AUTHOR]

PSP data processing, which primarily involves image alignment and image analysis, is a crucial element in obtaining accurate PSP results. There are two broad approaches to image alignment: the algebraic transformation technique, often called image-warping technique, and resection methodology, which uses principles of optical photogrammetry. Resection is found to have higher resolution, especially when large gradients in pressure or significantly three-dimensional regions have to be resolved, and even with flat models when the camera angles are large. A processing software based on these methodologies has been successfully developed and validated and is currently in use at the Experimental Aerodynamics Division of the National Aerospace laboratories, Bangalore. In this paper, we show the merits of resection methodology through two examples: (a) a wing-body model in transonic flow (M = 0⋅8) and (b) a simple delta wing in low supersonic flow (M = 1⋅8). The PSP system utilized for both the cases involve Optrod-B1 paint, a specially designed UV lamp for excitation and two scientific grade CCD cameras for imaging. Typical results are shown using both the algebraic transformation approach and resection methodology. [ABSTRACT FROM AUTHOR]

The present work aims at the characterization of the dust particles in South India through an image analysis of glass samples inclined at a tilt of 0° and 13° for four different exposure periods (approximately 30 to 40 days/exposure period). It aims as well at the study of the different factors influencing the accuracy of the image analysis of dust particles. The analysis of the shape factor reveals that the dust particles on tilted surface (13°) have regular shapes, and irregular shapes are more observed in horizontal surfaces. The size analysis of the dust particles with magnification of 20 × has revealed that the size distribution is in the range of 0–4 µm but more concentrated in the range of 0–1 µm. However, with 10 × magnification, larger particles are more detected. Furthermore, average results from three sample images seem to be more precise and representative than results from two images. The fractional coverage area of the dust particles on the sample has been calculated and compared with the transmittance losses. These two variables are found to be proportional with an R2 of 53%. Nevertheless, the comparison showed again that three images give better results with an R2 of 75% against 11% for two images. The results obtained in this study are very useful for the development of high precision soiling sensors that are based on image analysis and outdoor soiling microscopes, which are the main components for an efficient and economic cleaning of solar PV modules. [ABSTRACT FROM AUTHOR]

• Pore features of pervious concrete mixes are found through image analysis. • Pervious concrete mixes are prepared with non-conventional over-burnt brick aggregates. • Stereological analysis and mathematical morphology such as two-point correlation and granulometric distribution analysis are employed here. • Weibull distribution is the best way to fit the pore diameters of the pervious concrete mixes. The present study deals with the pore feature analysis of porous concrete mixes based on image analysis technique. The porous concrete mixes are prepared with over-burnt brick aggregate having different sizes and different aggregate gradations. The scarcity of natural aggregates in Tripura, India has forced the researchers to use locally available waste bricks as a coarse aggregate in construction purposes. However, the application of bricks in porous concrete is very rare and the porous distribution of porous mixes made of brick aggregates are not well-known. Hence, in this paper, initiatives are made to characterize the porous features of pervious concrete mixes using bricks as coarse aggregates. Stereological analysis and mathematical morphology such as two-point correlation and granulometric distribution analysis are employed in this paper. Statistical analysis is also conducted to identify the distribution of the pores and is observed that the 3-parameter Weibull distribution is the best way to fit the pore diameters of the pervious concrete mixes. Finally, the relative mean pore size is identified and the permeability of the pervious mix is obtained based on this image analysis. From the overall analysis, it is found that the image analysis can be an efficient way to identify the pore features of the porous concrete mixes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people. OBJECTIVE: An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR). METHOD: The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage. RESULTS: The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics. CONCLUSION: The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN). [ABSTRACT FROM AUTHOR]

This article seeks to address a thematic thread that remains relatively unexplored in historical disaster research—victimhood—through an analysis of publications by disaster relief funds and their supporters in the aftermath of the 1934 earthquake in Bihar in northern India. By examining the representations of victimhood, I aim to explore the historical significance of perceptions and constructions of victimhood in the late colonial period. Based on photographs, illustrations, and descriptions of suffering in images and texts, the article suggests that constructions of victimhood effectively relied on imagery that contained, on the one hand, an absence of bodies and, on the other, a feminized anthropomorphization of suffering. The narratives underlying such depictions of earthquake victims are based on a constitution of victimhood that relied on contemporary historical and culturally founded imageries. The analysis of images and texts focuses on how representations of disaster victims were effective in communicating suffering to audiences. I tentatively argue that historically and culturally founded tropes of what constituted a victim formed along two narratives of victimhood that appealed to a colonial and a nationalist readership respectively. These conceptualizations of victimhood formed the basis for collecting aid for relief and reconstruction, rather than the loss of life, dispossession, social marginalization, and displacement suffered by victims of the earthquake. [ABSTRACT FROM AUTHOR]

Ambient noise tomography is a powerful tool that has found increasing application in reservoir analysis and imaging. The Cachar fold belt in lower Assam, northeast India encompasses several wells under active hydrocarbon production, along with several dry wells. To overcome the lack of active seismic data over the entire fold belt, a passive seismic study was carried out to image the concealed three‐dimensional sub‐surface structures. The data were recorded from February to November 2011 by a network of 65 wideband seismometers spanning an area of about 40 × 60 km2. The data are crosscorrelated in the 2–5 s band, followed by phase‐weighted stacking to estimate noise correlation functions with surface wave signatures. The traveltimes picked from the frequency‐time analysis are utilized in a tomographic inversion for Rayleigh wave group velocities. The group velocity anomalies have a lateral resolution of ~ 3.5 × 5.5 km2 and variations of up to ±20%$\pm 20\%$ for each period. The group velocities are in turn inverted for S‐wave velocity distribution as a function of depth. The three‐dimensional S‐wave velocity tomograms reveal the tight anticlines and broad synclines, with high‐ and low‐velocity zones corresponding to structural highs and lows, respectively. The structural interpretation is supported for the part of the region with producing wells and covered by active seismic data, wherein the post‐stack time migrated seismic section shows anticlinal and synclinal features similar to those obtained from ambient noise tomography. The structures revealed by ambient noise tomography can help identify zones of interest to be targeted by active seismic surveys in the Cachar fold belt. [ABSTRACT FROM AUTHOR]

Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders' land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. Object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification. [ABSTRACT FROM AUTHOR]

Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial shade, and other minor faults. This causes the PV system to permanently fail and power losses. The power output in a partially shaded solar system is improved in this work by the introduction of a fault classifier based on thermal image analysis with a reconfiguration algorithm. For that purpose, the entire PV array is divided into two parts, with one of these being the male part and the other being the female part. MOSFET switches are used to build the switching matrix circuit that connects these parts. The Flir T420bx thermal camera captures thermal pictures, and MATLAB/Simulink® is used to extract the image properties. The pairing reconfiguration pattern is found using an algorithm based on image processing and the image attributes. The switching signals to the switching circuit are triggered by an Arduino controller. The image attributes of the thermal images may also be used to categorize PV system defects. This reconfiguration technique is easy, simple to use, and it can also be used to check the health of each PV module. The performance of the proposed work was validated using a 5 kW PV system with a 4 × 5 TCT array configuration at Sethu Institute of Technology's renewable energy lab in India. The proposed method was simulated using the MATLAB-Simulink software program, and the outcomes were verified on different hardware setups. [ABSTRACT FROM AUTHOR]

Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality and completeness can depend on the data and the methods with which they were produced. In this work we evaluate the effects of a variable visibility of the territory to map on the spatial distribution of the information collected in different landslide inventories prepared using different approaches in a study area. The method first classifies the territory in areas with different visibility levels from the paths (roads) used to map landslides and then estimates the landslide density reported in the inventories into the different visibility classes. Our results show that (1) the density of the information is strongly related to the visibility in inventories obtained through fieldwork, technical reports and/or newspapers, where landslides are under-sampled in low-visibility areas; and (2) the inventories obtained by photo interpretation of images suffer from a marked under-representation of small landslides close to roads or infrastructures. We maintain that the proposed procedure can be useful to evaluate the quality and completeness of landslide inventories and then properly orient their use. [ABSTRACT FROM AUTHOR]

Abstract: Recognition and classification of landslides is a critical requirement in pre- and post-disaster hazard analysis. This has been primarily done through field mapping or manual image interpretation. However, image interpretation can also be done semi-automatically by creating a routine in object-based classification using the spectral, spatial and morphometric properties of landslides, and by incorporating expert knowledge. This is a difficult task since a fresh landslide has spectral properties that are nearly identical to those of other natural objects, such as river sand and rocky outcrops, and they also do not have unique shapes. This paper investigates the use of a combination of spectral, shape and contextual information to detect landslides. The algorithm is tested with a 5.8m multispectral data from Resourcesat-1 and a 10m digital terrain model generated from 2.5m Cartosat-1 imagery for an area in the rugged Himalayas in India. It uses objects derived from the segmentation of a multispectral image as classifying units for object-oriented analysis. Spectral information together with shape and morphometric characteristics was used initially to separate landslides from false positives. Objects recognised as landslides were subsequently classified based on material type and movement as debris slides, debris flows and rock slides, using adjacency and morphometric criteria. They were further classified for their failure mechanism using terrain curvature. The procedure was developed for a training catchment and then applied without further modification on an independent catchment. A total of five landslide types were detected by this method with 76.4% recognition and 69.1% classification accuracies. This method detects landslides relatively quickly, and hence has the potential to aid risk analysis, disaster management and decision making processes in the aftermath of an earthquake or an extreme rainfall event. [Copyright &y& Elsevier]

Abstract: The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data. [Copyright &y& Elsevier]

Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-biophysical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives. [ABSTRACT FROM AUTHOR]

The 9th International Workshop on Combinatorial Image Analysis was held between 14 and 16 May, 2003 in Palermo (Italy).Prior meetings took place in Paris (France, 1991), Ube (Japan, 1992), Washington DC (USA, 1994), Lyon (France, 1995),Hiroshima (Japan, 1997), Madras (India, 1999), Caen (France, 2000) and Philadelphia (USA, 2001).The workshop focused on digital topology, discrete tomography, array, graph and parallel grammars, cellular automata, tilings and patterns, combinatorial methods in image analysis, combinatorial problems in the discrete plane, integer-only algorithms for image analysis, and non-conventional computing for image analysis, like genetic algorithms and global optimization.We are very pleased that P. Grirtzmann (Technische Universität München), Gabor T. Herman (City University of New York) and Reinhard Klette (The University of Auckland) accepted our invitations to speak at the Workshop.These proceedings contain thirty-one papers and they provide an exciting cross-section of current research in our field.The editors would like to thank the valuable work to the members of the program committee:Alfred M. Bruckstein, Technion Israel Institute of Technology, IsraelJean-Marc Chassery, TIMC-IMAG, FranceKatsushi Inoue, Yamaguchi University, JapanYung Kong, City University of New York, USAKenichi Morita, Hiroshima University, JapanMaurice Nivat, LIAFA, FranceMike Reed, Oxford University, UKRani Siromoney, Madras Christian College, IndiaPatrick S.P. Wang, Northeastern University, USAGerhard Woeginger, University of Twente, The NetherlandsThe editors thank also for the technical help given by Cesare Valenti, University of Palermo, ItalyAndrea Frosini, University of Siena, ItalyLászló Nyúl, University of Szeged, HungaryFinally, we would like to thank Peter Hammer for inviting us to publish the Workshop''s proceedings online as a volume of Electronic Notes in Discrete Mathematics, and for agreeing to devote a special issue of Discrete Applied Mathematics to IWCIA''2003. The DAM special issue will contain a refereed selection of papers submitted by Workshop presenters after the meeting. [Copyright &y& Elsevier]

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies. [ABSTRACT FROM AUTHOR]

Aim: To evaluate whether the residency training programs in India are meeting the expectations and aspirations of the present-day trainees.Introduction: Residents in training today are the neurosurgeons of tomorrow. Significant resources are dedicated to their training and education. Neurosurgical residency in India-when compared to the rest of the world-is relatively new. However, the structure of the Indian neurosurgical training programs has remained relatively unchanged since they were first introduced. In contrast, the treatment paradigms for neurosurgical conditions have changed rapidly in recent years, as new subspecialties have gained prominence.Methods: An online questionnaire based survey was prepared considering the common themes of neurosurgery residency. The links to the survey were mailed to residents in training all over the country. Results were evaluated after a minimum of 100 responses was collected. The responses were anonymized to ensure free and frank answers to the survey questions.Results: A total of 104 responses were received from all the major post-graduate training institutes of the country. The period of joining residency ranged from July 2010 to August 2018. Respondents generally agreed on the need for work hour regulations, the importance of history taking and clinical examination and the significance of imaging interpretation as major training goals. The residents were desirous of more hands-on opportunities during operative teaching. Deficiencies were recognized in the exposure to various upcoming subspecialties such as endovascular therapy, radiosurgery, epilepsy surgery, etc. The residents were generally satisfied with the pattern of examination system and showed a clear preference for electronic resources for reading and knowledge acquisition.Conclusion: The survey highlights the perception of the residents towards the training programs that they are enrolled into. Several lacunae and potential areas of improvement are brought to our attention, which have been necessitated by the recent advancements in technology affecting the neurosurgical treatment paradigms. The training programs need to keep pace with the aspirations and hopes of the residents so that the training experience is mutually fruitful for the trainees and educators. This is important to ensure that the present-day residents can step into their future roles with confidence and preparedness. [ABSTRACT FROM AUTHOR]

Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output. We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions. This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists. The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings. Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model's prediction can be improved. [ABSTRACT FROM AUTHOR]