Object-based approach for urban land cover mapping using high spatial resolution data

Wojtaszek, Malgorzata Verőné; Ronczyk, Levente; Mamatkulov, Zokhid; Reimov, Mamanbek

doi:10.1051/e3sconf/202122701001

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…Based on the results of the study, it was possible to determine the optimal areas for installing solar panels in any part of the country with increased efficiency using an automated geographic information system. At the same time, the amount of field research was reduced by 50% [6,14].…”

Section: Fig 2 Surface Temperature Map Of Samarkand Provincementioning

confidence: 99%

Development of a method for determining the optimal areas for alternative energy facilities based on GIS technologies

Ibragimov¹,

Inamov

Mukhamedayubova

et al. 2023

E3S Web Conf.

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Acquisition of alternative energy resources using the modern Global Mapper software on an electronic digital map is an important process. This article analyses the possibility of using maps in the implementation of internationally acknowledged projects, as well as interactive services to the government, and the possibility of mapping alternative energy sources, synthesizing data, and developing management decisions through the development of innovative technologies. The research in this study is about determining optimal locations for the installation of solar panels and wind turbines. For the tests, Tashkent and Samarkand provinces were selected. Initially, the elevation of the provinces was determined by remote sensing and the elevation surface was created by interpolation. According to the data obtained from the regional hydrometeorological stations, the air temperature in the regions was taken and geovisualized as a vector layer. At the same time, the wind speed was mapped using the method of geostatistical analysis, which observed the annual average wind speed. On the basis of the highest parts of the earth’s surface and in areas with high temperatures, raster calculations were performed and the method for determining the most optimal place for installing solar panels was improved.

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Section: Fig 2 Surface Temperature Map Of Samarkand Provincementioning

confidence: 99%

Development of a method for determining the optimal areas for alternative energy facilities based on GIS technologies

Ibragimov¹,

Inamov

Mukhamedayubova

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…In recent years, the number of remote sensing satellites has surged dramatically, providing massive geographic imagery for almost every corner of the Earth's surface [5,6]. In extracting urban land cover information from remote sensing data, researchers often consider spatial resolution more important than spectral resolution [7,8]. This is because spatial resolution can reflect the Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

Lu,

Li,

Zhang

et al. 2024

Remote Sensing

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Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.

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“…In the last decade, the scale parameter and shape compactness have received high attention in research on the optimal selection of segmentation parameters [9,10]. The multiresolution image segmentation algorithm in eCognition has been widely used for urban mapping applications.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications

Ikokou,

Malale

2024

Geomatics

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Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process.

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Object-based approach for urban land cover mapping using high spatial resolution data

Cited by 14 publications

References 10 publications

Development of a method for determining the optimal areas for alternative energy facilities based on GIS technologies

Development of a method for determining the optimal areas for alternative energy facilities based on GIS technologies

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications

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