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Automated extraction of mining-induced ground fissures using deep learning and object-based image classification

EARTH SURFACE PROCESSES AND LANDFORMS 2024年第7期49卷 2189-2204页

作者： Huangfu, Wenchao Qiu, Haijun Cui, Peng Yang, Dongdong Liu, Ya Ullah, Mohib Kamp, Ulrich Northwest Univ Coll Urban & Environm Sci Xian 710127 Peoples R China Northwest Univ Inst Earth Surface Syst & Hazards Xian Peoples R China Northwest Univ Shaanxi Key Lab Earth Surface Syst & Environm Carr Xian Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Chengdu Peoples R China Univ Michigan Dearborn Dept Nat Sci Earth & Environm Sci Discipline Dearborn MI USA

Accurate extraction of ground fissures caused by intense coal mining has the potential to significantly improve the efficiency of environmental monitoring in mining areas. However, the extraction results using previous methods have often exhibited issues of discontinuity and substantial deviation from ground truth data, resulting in low extraction accuracy. In this study, a novel approach, ENVINet5-OBIC, for extracting ground fissures in mining areas is proposed, which integrates object-based image classification (OBIC) with the pixel-based deep learning model ENVINet5. ENVINet5-OBIC uses OBIC to segment high-resolution unmanned aerial vehicle (UAV) images across different scales, effectively considering shape, texture and correlative information between adjacent pixels. Furthermore, by utilizing homogeneous objects as building blocks, it establishes a deep learning model for the automated extraction of ground fissures. Experimental results show that ENVINet5-OBIC performs better when compared with OBIC, U-Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. In addition, the ground fissure area extracted by ENVINet5-OBIC closely aligns with ground truth data. This study provides a more effective method for automatic extraction of ground fissures, which improves the efficiency of environmental monitoring in mining areas. This research proposes the ENVINet5-OBIC approach, which combines the deep learning model ENVINet5 with object-based image classification (OBIC). ENVINet5-OBIC has better performance in ground fissure extraction when compared with OBIC, U-Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. image

关键词： automatic extraction deep learning ground fissure object-based image classification UAV image

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PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH object-based image classification 23

PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DE...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Sabuncu, A. Avci, Z. D. Uca Sunar, F. Bogazici Univ Kandilli Observ Istanbul Turkey Bogazici Univ Earthquake Res Inst Geodesy Dept Istanbul Turkey Istanbul Tech Univ Dept Astronaut Engn Fac Aeronaut & Astronaut Istanbul Turkey Istanbul Tech Univ Geomat Engn Dept Istanbul Turkey

Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw=7.1) was occurred on October 23th, 2011;at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 ((C) Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.

关键词： Ercis-Van Earthquake object-based image classification Building Damage Detection

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A Study on the object-based High-Resolution Remote Sensing image classification of Crop Planting Structures in the Loess Plateau of Eastern Gansu Province

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REMOTE SENSING 2024年第13期16卷 2479页

作者： Yang, Rui Qi, Yuan Zhang, Hui Wang, Hongwei Zhang, Jinlong Ma, Xiaofang Zhang, Juan Ma, Chao Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Remote Sensing Gansu Prov Lanzhou 730000 Peoples R China

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors-including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other relat

关键词： Gaofen images object-based image classification random forest convolutional neural network crop classification

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A method on mapping the distribution of photovoltaic power stations in complex terrain regions

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RENEWABLE ENERGY 2025年 247卷

作者： Mao, Hongzhi Liu, Weili Chen, Xie Tian, Zhiyong Zarrella, Angelo Luo, Yongqiang Fan, Jianhua Wu, Wentao Huazhong Univ Sci & Technol Sch Environm Sci & Engn Hubei Key Lab Multimedia Pollut Cooperat Control Y 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Univ Padua Dept Ind Engn Via Venezia 1 I-35131 Padua Italy Tech Univ Denmark Dept Civil & Mech Engn Brovej 118 DK-2800 Lyngby Denmark Univ Canterbury Dept Civil & Nat Resources Engn Christchurch New Zealand

With the goal of achieving carbon neutrality, the number of new photovoltaic (PV) installations worldwide has surged in recent years. Accurate statistics on the area and distribution of centralized solar PV installations are essential. Due to land use constraints and poverty alleviation policies, an increasing number of PV stations are being installed in mountainous areas in China. The fragmented distribution of these PV stations presents challenges for remote sensing identification. This study proposes a method based on object-based image Analysis (OBIA) and the Cascade Random Forest Classifier, utilizing Sentinel-1/2 imagery. The method is capable of extracting PV stations across diverse terrains, including mountains, plateaus, and plains. Specifically, five different scenarios with varying feature combinations (including spectral, index, geometric, and texture features from Sentinel-1 and Sentinel-2) were compared. A second-level random forest classifier, trained on false-positive samples, was employed to enhance the model's robustness. The results indicate that the spectral features of Sentinel-1, along with index and geometric features, positively influence identification accuracy, while texture features showed less impact. Independent test results demonstrated an overall classification accuracy of 99.9904 %, with a producer accuracy of 92.73 % and a user accuracy of 81.38 % for PV stations in a complete city region. Finally, a 2024 map of centralized PV installations was created for five cities west of Beijing, where the proportion of total PV area in mountainous regions is 45 %. This method represents the first approach capable of identifying PV stations in complete regions with complex terrain, including mountains, without the need for manual post-processing.

关键词： Photovoltaic stations object-based image classification Sentinel-1/2 Complex terrains

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A comparative study of the surface level changes of Urmia Lake and Aral Lake during the period of 1988 to 2018 using satellite images

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FRONTIERS OF EARTH SCIENCE 2022年第1期19卷 108-119页

作者： Khorshiddoust, Ali Mohammad Patel, Nilanchal Khalilzadeh, Elnaz Bostanabad, Saeed Armaghan Tajbar, Sapna Univ Tabriz Dept Climatol Tabriz 51664 Iran Berla Inst Technol Dept Remote Sensing Ranchi 835215 Bihar India

Internal lakes are considered as the ecological environments and the monitoring and evaluation of which can be considered as a matter in the national development and natural resources management (Rasouli et al., 2008). The changes in the water levels of lakes and the variations of the land use and cover along with the margins and the association of these changes with natural and human factors in Urmia Lake and Aral Lake are much-discussed and taken into consideration. In this study, a comparison was made between Urmia Lake and the Aral Lake over a period of 28 years, and the first one being suffered from declining water levels and drastic changes due to many factors, including, most specifically, human intrusion and mismanagement. For this purpose, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5 and 8 satellite images were utilized for 1988 and 2018 and object-based image classification was applied in six classes, i.e. water body, arid, agriculture, salt marsh, salt wet and built with the acceptable accuracy of 89.23 (1988) and 85.8 (2018) percent for land use maps of the Urmia Lake and 92.46 (1988) and 95.15 (2018) percent for the Aral Lake. The overall accuracy of classification which represents the functioned credit classification for the maps should be more than 85% (Anderson et al., 1976). The classification showed that during the study period both of the lakes faced declination of water and increasing of salt levels. The trend of agricultural and built-in lands has been rising especially in Urmia Lake. Results indicated that the creation of the agricultural lands and human levels in recent decades has caused the increase in water consumption which in turn has reduced the amount of water entering to the Urmia Lake and has created hard conditions such as the dryness of the lake today.

关键词： change detection MODIS Landsat object-based image classification Urmia Lake and Aral Lake Iran

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Pixel-based vs. object-based anthropogenic impervious surface detection: driver for urban-rural thermal disparity in Faridabad, Haryana, India

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GEOCARTO INTERNATIONAL 2022年第25期37卷 8543-8566页

作者： Kumar, Sunil Midya, Kousik Ghosh, Swagata Singh, Sultan Amity Univ Amity Inst Geoinformat & Remote Sensing AIGIRS Noida Uttar Pradesh India Ajay Kumar Garg Engn Coll Dept Elect & Commun Engn Ghaziabad Uttar Pradesh India Haryana Space Applicat Ctr Node Gurugram Haryana India

Anthropogenic impervious surface area (AISA) expansion with its impact on temperature is current research trend. Majority of earlier research focused on land surface temperature (LST) versus AISA relationship in specific year independently. Therefore, present research is a novel attempt to study Delta LST-Delta AISA correlation during 2012-2017 in core urban, semi urban, and rural region of Faridabad. Results demonstrate applicability of object-based image classification (OBIC) over pixel-based image classification (PBIC) to detect AISA with higher accuracy (OBIC: 2012-94%, 2017-93%, PBIC: 2012-79%, 2017-84%). Degree of non-linearity of LST-AISA relationship increases from urban to rural region, evident from the coefficient of AISA% (0.03-core urban, 0.07-semi-urban, 0.08-rural). Absolute temperature is low in land with shallow ground water depth up to 10 m. Higher percentage of land with shallow water depth causes significant increase in (Delta LST/Delta AISA) in rural region. Consequently, authors suggest avoiding acquisition of land with water table depth 3-10 m for future impervious surface development.

关键词： Anthropogenic impervious surface area land surface temperature linear regression LISS-IV object-based image classification

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Solar park detection from publicly available satellite imagery

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GISCIENCE & REMOTE SENSING 2022年第1期59卷 461-480页

作者： Plakman, Veerle Rosier, Job van Vliet, Jasper Vrije Univ Amsterdam Earth & Climate Amsterdam Netherlands Vrije Univ Amsterdam Inst Environm Studies Amsterdam Netherlands

The rapid increase in large-scale photovoltaic installations, or solar parks, causes a need to monitor their amount and allocation, and assess their impacts. While their spectral signature suggests that solar parks can be identified among other land covers, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery, which has the advantage of requiring relatively little training data and being easily extendable to large spatial extents and new areas. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm in Google Earth Engine. Thereafter, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2. These features are subsequently used to train and validate a range of RF models to select the most promising model setup. The training datasets consisted of subsampled presence/absence data, oversampled presence/absence data, and multiple land-cover categories. The best-performing model used an oversampled dataset trained on all 10- and 20- meter resolution spectral bands and the radar backscatter properties of one period. Independent test results show an overall classification accuracy of 99.97% (Kappa: 0.90). For this result, the producer accuracy was 85.86% for solar park objects and 99.999% for non-solar park objects. The user accuracy was 92.39% for solar park objects and 99.999% for non-solar park objects. These high classification accuracies indicate that our approach is suitable for transfer learning and is able to detect solar parks in new study areas.

关键词： Photovoltaics object-based image classification random forest transfer learning land use land cover

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Polycentric urban growth and identification of urban hot spots in Faridabad, the million-plus metropolitan city of Haryana, India: a zonal assessment using spatial metrics and GIS

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2022年第6期24卷 8246-8286页

作者： Kumar, Sunil Ghosh, Swagata Singh, Sultan Amity Univ Amity Inst Geoinformat & Remote Sensing AIGIRS Sect 125 Noida 201313 Uttar Pradesh India Mini Secretariat Haryana Space Applicat Ctr Node Gurgaon New Labour Court BldgSect 11 Gurugram 122001 Haryana India

Polycentric urban structure is the recent trend of new-age sustainable cities in India. Limited research is reported focused on quantitative analysis of polycentric structure at the intracity level. In the present research, a detailed database has been generated using multitemporal satellite imageries. Zone-wise quantitative analysis, supported by the database, exhibits clear evidence of transit-oriented polycentric urban growth around Old Faridabad, NIT and Ballabhgarh. Linear urban expansion close to highways, railways, metro ways and urban extension as Greater Faridabad has led to the vertical expansion of the city. Highly variable urban density has been observed in the southern and south-western zones. Shannon's entropy has identified even distribution of built-up around Old Faridabad, whereas clustering of high urban density around NIT and Ballabhgarh. Getis-Ord Gi* statistics have identified a consistent increase of urban hot spot areas from 21.92% (2007), 26% (2012) to 27.48% (2017). The present study suggests planners and policymakers to improve the connectivity between the upcoming urban centre Greater Faridabad, three existing urban centres and other under-developed districts of Haryana to achieve balanced development. Such a step would contribute to the sustainable solutions for various issues over the hot spot around the current urban centres and achieve inclusive urban growth in Faridabad. Moreover, current findings contribute to building a model framework for analysing urban growth patterns even for other satellite cities of India.

关键词： Urban density Polycentricity Hot spots Getis-Ord Gi* statistic Shannon's entropy object-based image classification GIS

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Spatiotemporal changes of glacier and seasonal snow fluctuations over the Namcha Barwa-Gyala Peri massif using object-based classification from Landsat time series

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 177卷 21-37页

作者： Guo, Shanchuan Du, Peijun Xia, Junshi Tang, Pengfei Wang, Xin Meng, Yaping Wang, Hua Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Minist Nat ResourcesKey Lab Land Satellite Remot Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China RIKEN Ctr Adv Intelligence Project AIP Geoinformat Unit Tokyo 1030027 Japan Nyingchi Meteorol Serv Nyingchi 860000 Tibet Peoples R China

The glacier environment in the Namcha Barwa-Gyala Peri (NBGP) massif is regarded as one of the most sensitive areas to climate change, yet the change estimates remain inadequate due to the limited knowledge of the complex debris-covered glacier environment and the lack of available remote sensing data caused by continuously cloudy weather. To examine the changes of the complex glacial environment, a multiple hierarchical object- and rule-based classification (MHORC) scheme is proposed by combining Landsat time series images and topographic data. object-based image analysis (OBIA) is introduced due to its capabilities of handling data contextually and hierarchically and using multi-source data jointly so that multiple hierarchical rule-based classification can be developed to distinguish the objects with similar spectrum such as debris-covered glaciers and their surrounding terrain. All the available Landsat images from 1987 to 2019 are filtered by the cloud cover condition (less than 10%) and accordingly used in the MHORC to obtain spatial and temporal information of the glacier and snow over the NBGP massif. The images collected in the snow-free season are used to estimate the glacier extent state and the historical changes, and those in the snow-covered are used to estimate the snow cover fluctuation using harmonic analysis. The results show that the NBGP contains 65 glaciers(>= 0.1 km(2)) with a total area of 462.7 km(2), among which valley glaciers account for 21.5% in number but cover an area of 354.2 km(2), more than 76% of the total area. An accelerating reduction trend of the glacier area is confirmed as the area change velocity is -0.62 km(2) yr(-1) between 1999 and 2003 and reach -2.95 km(2) yr(-1) between 2003 and 2015. The seasonal snow cover area shows a reduction in the secular trend and an increase in fluctuation amplitude. Both glacier and seasonal snow display heterogeneity in the spatial and temporal patterns. Such heterogeneous dynamics might be r

关键词： Glacial environment changes object-based image classification Landsat time series Google Earth Engine The Namcha Barwa-Gyala Peri massif

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Attentive Spatial Temporal Graph CNN for Land Cover Mapping From Multi Temporal Remote Sensing Data

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IEEE ACCESS 2021年 9卷 23070-23082页

作者： Censi, Alessandro Michele Ienco, Dino Gbodjo, Yawogan Jean Eudes Pensa, Ruggero Gaetano Interdonato, Roberto Gaetano, Raffaele Univ Montpellier UMR TETIS INRAE F-34000 Montpellier France Univ Turin Dept Comp Sci I-10124 Turin Italy CIRAD UMR TETIS F-34090 Montpellier France

Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the spatial patterns that characterize these data structures. Focusing on land cover classification (or mapping) tasks, the majority of approaches dealing with SITS data only considers the temporal dimension, while the integration of the spatial context is frequently neglected. In this work, we propose an attentive spatial temporal graph convolutional neural network that exploits both spatial and temporal dimensions in SITS. Despite the fact that this neural network model is well suited to deal with spatio-temporal information, this is the first work that considers it for the analysis of SITS data. Experiments are conducted on two study areas characterized by different land cover landscapes and real-world operational constraints (i.e., limited labeled data due to acquisition costs). The results show that our model consistently outperforms all the competing methods obtaining a performance gain, in terms of F-Measure, of at least 5 points with respect to the best competing approaches on both benchmarks.

关键词： Time series analysis Task analysis Satellites image segmentation Data models Earth Convolutional neural networks Spatial temporal graph convolutional neural network attention-based neural network object-based image classification satellite image time series land cover classification deep learning

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