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Urban extraction mapping has become increasingly important in recent years and particularity extraction urban features based on remotely sensed data such as highresolution imagery and LiDAR data. Though the researchers used the high spatial resolution image to extract urban area but he methods are still complex and still there are challenges associated with combining data that were acquired over differing time periods using inconsistent standards. So, this study will focus on the extraction of urban area based on an object-based classification method with integration of Quickbird satellite image and digital surface elevation (DSM) extracted from LiDAR data for the Rusafa city of Baghdad, Iraq. All the processes were done in eCognition and ArcGIS software for feature extraction and mapping, respectively. The overall methodological steps proposed in this research for the extraction of urban area using object-based method. In addition of that both the image data and LiDAR-derived DSM were integrated based on the eCognition software for extraction urban map of Rusafa city, Baghdad. Finally, the results indicated that the Artificial Neural Networks (ANN) model achieved the highest training and testing accuracies and performed the best compared to RF and Support Vector Machines (SVM) methods. And also, the results showed that the Artificial Neural Networks (ANN) had capability to extract the boundaries of the buildings and other urban features more accurately than the other two methods. This could be interpreted as the Artificial Neural Networks (ANN) model can learn complex features by the optimization process of the model and its multi-level feature extraction property

We can automatically deduce the type of an object using the auto specifier. The auto specifier deduces the type of an object based on the object’s initializer type.

In order to overcome the problems of poor timeliness and low accuracy of mining existing in traditional methods, this paper designs a bit-object based maximum frequent pattern mining method for intensive cloud computing data. After judging the support number according to the bit object of the maximum frequent pattern, the intensive cloud computing data is accurately collected according to the difference between the load value of cloud data and the true value of load, so as to improve the accuracy of subsequent mining results, and then the maximum frequent pattern of data is accurately mined by combining the bit object. Experimental results show that the maximum time to generate mining results is only 4.6 s, the maximum bit error rate of output results is only 7%, and the maximum memory occupancy is only 3.90%. The above results show that this method is more suitable for practical excavation.

In this paper, the approach Linear Spectral Mixture Analysis and Object-Based Image Analysis (LSMA + OBIA) and Iterative Self-Organizing Data Analysis Technique and Object-Based Image Analysis (ISODATA + OBIA) are evaluated, for optimizing land cover mapping in high mountain areas from Landsat-8 multispectral images. Both approaches are applied to generate in a semiautomatic and unsupervised way a land cover map of the Santurban-Berlin moorland, located in Colombia as a case study to carry out the evaluation. It has been found that LSMA + OBIA allows the generation of a land cover classification with a maximum global reliability of 88% compared to a reliability of 79% with ISODATA + OBIA.

Remote sensing has long been used to study wildlife; however, manual methods of detecting wildlife in aerial imagery are often time-consuming and prone to human error, and newer computer vision tec...

The object detection algorithm is mainly focused on detection in general scenarios, when the same algorithm is applied to drone-captured scenes, and the detection performance of the algorithm will be significantly reduced. Our research found that small objects are the main reason for this phenomenon. In order to verify this finding, we choose the yolov5 model and propose four methods to improve the detection precision of small object based on it. At the same time, considering that the model needs to be small in size, speed fast, low cost and easy to deploy in actual application, therefore, when designing these four methods, we also fully consider the impact of these methods on the detection speed. The model integrating all the improved methods not only greatly improves the detection precision, but also effectively reduces the loss of detection speed. Finally, based on VisDrone-2020, the mAP of our model is increased from 12.7 to 37.66%, and the detection speed is up to 55FPS. It is to outperform the earlier state of the art in detection speed and promote the progress of object detection algorithms on drone platforms.

With the problem of informal settlements in the Philippines, mapping such areas is the first step towards improvement. Object-based image analysis (OBIA) has been a powerful tool for mapping and feature extraction, especially for high-resolution datasets. In this study, an informal settlement area in UP Diliman, Quezon City was chosen to be the subject site, where individual informal settlement structures (ISS) were delineated and estimated using OBIA. With the help of photogrammetry and image enhancement techniques, derivatives such as elevation model and orthophotos were produced for easier interpretation. An initial rule-set was developed to remove all non-ISS features from the base image–utilizing spectral values and thematic layers as main classifiers. This classification technique yielded a 94% accuracy for non-ISS class, and 92% for the possible ISS class. Another rule-set was then developed to delineate individual ISS based on the texture and elevation model of the area, which paved the way for the estimation of ISS count. To test the robustness of the methodology developed, the estimation results were compared to the manual count obtained through an online survey form, and the classification and delineation results were assessed through overall and individual quality checks. The estimation yielded a relative accuracy of 60%, which came from the delineation rate of 63%. On the other hand, delineation accuracy was calculated through area-based and number-based measures, yielding 58% and 95%, respectively. Issues such as noisy elevation models and physical limitations of the area and survey done affected the accuracy of the results.

The increasing use of private transportation, now more and more public places that provide vehicle storage facilities in their area. This vehicle storage is better known as parking by the community. In Article 1 No. 14 of the Regional Regulation of Indramayu Regency Number 16 of 2017 concerning Parking Management which states that parking can be interpreted as a state of stopping or not moving for a while and being abandoned by the driver. The existence of a parking lot is very helpful for the community, especially for those who have vehicles, you can imagine if there is no parking space. In the concept of this scientific paper using the normative juridical research method. As well as using data collection techniques carried out using descriptive analysis techniques, with secondary data sources, which include primary legal materials such as laws and regulations relating to the Regional Regulation of Indramayu Regency Number 16 of 2017 concerning Parking Management. The purpose of this study is to find out where special parking spaces can be established in the Cimanuk Indramayu culinary tourism object based on Regional Regulation Number 16 of 2017 concerning Parking Implementation and to find out the management of parking special areas in Indramayu based on Regional Regulation of Indramayu Regency Number 16 of 2017 concerning Parking Implementation. The results of this study indicate that in the implementation of Parking Management in Indramayu Regency, it has been regulated in the Regional Regulation of Indramayu Regency Number 16 of 2017 concerning Parking Management and has been in accordance with applicable regulations. But in its implementation, this is not like that because in implementation, the parking lot is a utility, only useful for local governments for regional income but for the community it is very burdensome.

The land cover classification of very high-resolution (VHR) remote sensing images is a challenging task. VHR images depict many complex objects with various shapes in complicated contexts. The deep...

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 map...

The potential of agricultural land in Indonesia is still very wide so that the agricultural sector is still one of the incomes for most Indonesian people. The agricultural land sector is widely distributed in various regions in Indonesia, one of which is in the East Oku Regency, South Sumatra. As an agricultural area, the majority of the population in East Oku Regency choose their livelihood as farmers. However, the distribution of agricultural land has not been mapped, which can provide information about the location of agricultural land, especially in East Oku Regency. In this study, the development of a web-based Agricultural Land Geographic Information System in East OKU Regency was carried out. The geographic information system built can display information in the form of spatial and non-spatial data that describes an object based on the state of the earth. The method used in making this system is object-oriented method. With the development of this Geographic Information System, it is hoped that it will be able to provide information for the community about the distribution of the location and area of ​​agricultural land, especially in the East OKU Regency area.

The objective of this work is to construct a cooperative neural network where every neuron is a minimal computational processing routine. This paper shows the implementation of such a network for solving the familiar shortest path problem without compromising the objective set for the Object-Based Model (OBM). This paper also throws light on biological observations about the relationship between the time and functioning of a brain. Further, this problem maps the distance/costs in a graph into time and uses it to be the activation time of a neural routine. The cooperative functioning of such neurons producing the result for the shortest path problem is explained with its algorithm. The successful functioning of the implementation of the algorithm is demonstrated with suitable timing diagrams. The theoretical analysis of the algorithm is also provided to show why this model can be considered as an adaptive neural network.

National stratification maps are essential to improve forest management systems. For the Democratic Republic of the Congo, the existing maps derived from remote sensing techniques do not allow an optimal representation of the diverse land cover classes constituting the national stratification scheme. This situation is inherent to the cloud persistence, the seasonality effects and the spatial resolution of the input satellite imagery used that is not always adequate for the discrimination of certain land cover classes. This paper explores a cloud-based median luminance best pixel approach to obtain a cloud-free mosaic of optimal quality. The mosaic produced has necessitated nearly 2,500 Landsat scenes and a following object-based classification enabled the generation of a stratification map for the year 2000 according to the national stratification theme. A stratified random sampling approach that required 1,141 reference samples allowed estimating the map accuracy at 79.32%. Land cover classes areas computed using standard good practices recommendations to estimate land areas indicated that the dense moist forest area was about 158,810,975 ± 7,460,671 ha representing 68.40% ± 3.21% of the country area. Thanks to the free, user-friendly and cloud-based platforms for satellite images processing, the methodology implemented is easily replicable for other tropical countries. &nbsp

Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six...

The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance and low inter-class separability in the fine spatial resolution (FSR) remotely sensed imagery. This makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging task. To mine effectively the rich spectral and spatial information in FSR imagery, this paper proposed a Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) that classifies images at the object level by taking segmented objects (crop parcels) as basic units of analysis, thus, ensuring that the boundaries between crop parcels are delineated precisely. These segmented objects were subsequently classified using a CNN model integrated with an automatically generated scale sequence of input patch sizes. This scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger scales. The effectiveness of the SS-OCNN was investigated using two heterogeneous agricultural areas with FSR SAR and optical imagery, respectively. Experimental results revealed that the SS-OCNN consistently achieved the most accurate classification results. The SS-OCNN, thus, provides a new paradigm for crop classification over heterogeneous areas using FSR imagery, and has a wide application prospect.

The increasing area of Plastic-Mulched Farmland (PMF) is aggravating the conflict between agricultural development and environmental protection. The spatial distribution of PMF requires an effectiv...

The difference image that represents the change levels is pivotal in unsupervised change detection tasks. An object-based graph model is proposed in this paper to generate more reliable difference ...

The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR during April–September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern United States during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE. HRRR tends to overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds and large objects at high-reflectivity thresholds. HRRR tends to either underforecast objects in the southern and central plains or has a correct frequency bias there, whereas it overforecasts objects across the southern and eastern United States. Attribute comparisons reveal the inability of the HRRR to fully resolve convective-scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts. Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke skill score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

Vernal pools are small, temporary, forested wetlands of ecological importance with a high sensitivity to changing climate and land-use patterns. These ecosystems are under considerable development ...

The aim of this study is to evaluate the classification performances of land use/land cover (LULC) classification methods by comparing the results of pixel and object-based classification approaches on RapidEye satellite image. Pixel-based classification was carried out in ERDAS Imagine 10.4 using the Maximum Likelihood-supervised approach, whilst object-based classification was performed in e-Cognition Developer 64 using the nearest neighbour-supervised classification method. A LULC map of eight classes was created in both methods. While the accuracy for thematic LULC classes varied in both methods, the overall accuracy and kappa values of LULC maps for pixel and object-based classification methods were 58.39%-0.45 and 89.58%-0.86, respectively. Accuracy assessments and comparative results showed that object-based classification gives better results for thematic LULC classes as well as the overall accuracy of LULC maps. Even though pixel-based classification method was good at mapping many thematic classes, there were misclassifications between natural/semi-natural LULC classes. These results can be attributed to parameters set by users, such as the number of control points, etc. However, the capacity of object-based classification method to include auxiliary data (e.g. DEM, NDVI) increases the accuracy of LULC maps with high-resolution satellites.

A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data.

A novel potential illegal construction (PIC) detection method by bitemporal unmanned aerial vehicle (UAV ) image comparison (change detection) within building roof areas is proposed. In this method, roofs are first extracted from UAV images using a depth-channel improved UNet model. A two-step change detection scheme is then implemented for PIC detection. In the change detection stage, roofs with appearance, disappearance, and shape changes are first extracted by morphological analysis. Subroof primitives are then obtained by roof-constrained image segmentation within the remaining roof areas, and object-based iteratively reweighted multivariate alteration detection (IR-MAD ) is implemented to extract the small PICs from the subroof primitives. The proposed method organically combines deep learning and object-based image analysis, which can identify entire roof changes and locate small object changes within the roofs. Experiments show that the proposed method has better accuracy compared with the other counterparts, including the original IR-MAD, change vector analysis, and principal components analysis-K-means.

Precise and regularly updated maps of surface water extent are essential for wetland management. Since it is often challenging to obtain water extent information through ground surveys due to acces...

Technological stresses are an urgent challenge for winter crops and in particular rapeseed in industrialproduction and in the mass use of agrochemicals and plant protection products. Effective resuscitationmeasures are possible only in the initial stages of the growing season and require reliable and accessibleinformation about the condition of crops. The purpose of the work is to develop an index based on the resultsof analysis of multispectral images with high spatial resolution, obtained by UAVs for the identificationof technological stresses. During field research on October 30, 2019, it was found that in the affected areas,the plants have an abnormal color of the two lower leaves, namely yellow and red. To identify affectedplants, it was proposed to use an image of the object based on the ratio of several channels simultaneously, which allow to distinguish between affected and healthy plants, soil and leaves of abnormal color. Itis proposed to use the RRL (rape red leaf) indices, which are an indicator of the technological nature ofstress, namely RRLgr, designed exclusively for the optical range and the RRLm index (green — G, red — R,border red — Re and near infrared — NIR). Such indices are convenient for monitoring the condition ofrapeseed crops and automated data processing. It was found that when monitoring rapeseed crops in thegrowing season of 6–8 leaves affected by technological stress, using Slantrange 3p height of 100 m, for the indices RRLgr and RRLm is characterized by abnormal leaf color, which was recorded in 1.5 and 2.1%of the total area of plants on the site, respectively. The use of multispectral analysis makes it possible todifferentiate the identification of technological stresses with different manifestations of impression. Ata standard flight altitude for Slantrange 3p of 100 m with the fixation of anomalous color in 1.5% of thetotal area of winter oilseed rape plants is the basis for the organization of additional ground inspection ofthe identified areas of winter oilseed rape crops.

The shape of a vertical cylinder resembles the classic form of a spar platform. Spar platforms are floating platforms that are successfully used in waters of great depths and have several advantages that mean they are readily used in the oil industry. Many of these advantages are also relevant to their application for offshore wind turbines, which is currently being considered. In the hydrodynamic analysis of spar platforms, the determination of their hydrodynamic coefficients plays an important role. They can be determined based on the free decay test. The study presents a method for determining the hydrodynamic coefficients of an object based on the free decay test. The results of free oscillation calculations with the help of numerical fluid mechanics tools are presented and compared with the results of the experiment and analytical solution. The application of determined coefficients and their significance for floating platforms are discussed. The influence of change in the form of an additional damping element on the behaviour of spar structures is shown.

Wetlands of coastal Gabon provide many ecosystems services including flood protection, water purification and wildlife habitat. Effective sustainable management of this coastal zone is hindered by a lack of accurate wetland maps. Here we describe a novel method used to map the wetland ecosystems of nearly 100 000 km2 of wetland and upland habitat mosaic in the delta of the Ogooué River using an object‐based classification of optical and radar satellite imagery based on training data collected from unmanned aerial vehicle and a post‐classification accuracy assessment using helicopter‐based video. We identified 15 land cover classes, of which nine were wetland. A stratified random sample accuracy assessment of the final classification yielded an overall accuracy of 0.80. Despite the important role that wetland habitats play for wildlife and ecosystem functioning across the region, our results indicate these wetlands cover only 22% of the project area. As expected, most of the wetland habitats are found close to major water bodies, including the Ogooué River, estuaries near the cities of Libreville and Port Gentil and coastal lagoons to the south of these cities. When considering the six Wetlands of International Importance designated under the Ramsar Convention within the project area, only 33% of mapped wetlands fall within the Ramsar site boundaries and only 10% of mapped wetlands fall within protected areas. Furthermore, within the Ramsar sites, only 31% of the land cover was classified as wetland. In order to better manage these wetland resources, more effective Ramsar boundaries would include the extensive wetland habitats found along the coast from Port Gentil south to Loango National Park. These data are now available for managers to improve wetland management within designated Ramsar sites and improving protection designations for vulnerable habitats, for example by protecting wetland connectivity and other ecosystem processes.