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ISPRS Technical Commission VII Symposium - 100 Years ISPRS - Advancing Remote Sensing Science

作者： Hoefle, B. Hollaus, M. Heidelberg Univ Dept Geog D-69120 Heidelberg Germany Vienna Univ Technol Inst Photogrammetry & Remote Sensing A-1040 Vienna Austria

In this paper, a new GIS workflow for fully automated urban vegetation and tree parameter extraction from airborne LiDAR data is presented. The strengths of both raster- and point cloud-based methods are combined, in order to derive a vegetation map layer as well as single tree parameters (e. g. tree height and crown width) in an efficient way. The workflow is implemented in GRASS GIS making use of standard GIS functionality and newly developed tools providing access to point cloud analysis. An edge-based segmentation delineates potential tree crowns, which are further aggregated to single trees or group of trees by using local topological information (e. g. percentage of outline touched by neighbors) and constraints on segment geometry (e. g. shape of segments). Furthermore, the classification makes use of segment attributes that have been extracted from the full-waveform point cloud (e. g. percentage of first echoes, echo width and signal amplitude). A representative study area in the City of Vienna is used to demonstrate the applicability of the developed object-based GIS workflow. Buildings and vegetation objects could be separated with high accuracy, where at maximum 14% of classified vegetation segments confuse with buildings (mainly building edges). Concluding, the unique high density (50 pts/m(2)) full-waveform LiDAR data open a new scale in 3D object extraction but demanding for novel strategies in object-based raster and point cloud analysis.

关键词： Airborne LiDAR Vegetation mapping Open source GIS object-based image analysis

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Advancing Seabed Bedform Mapping in the Kuźnica Deep: Leveraging Multibeam Echosounders and Machine Learning for Enhanced Underwater Landscape analysis

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REMOTE SENSING 2025年第3期17卷 373-373页

作者： Janowski, Lukasz Gdynia Maritime Univ Maritime Inst Roberta De Plelo 20 PL-80548 Gdansk Poland

The ocean, covering 71% of Earth's surface, remains largely unexplored due to the challenges of the marine environment. This study focuses on the Ku & zacute;nica Deep in the Baltic Sea, aiming to develop an automatic seabed mapping methodology using multibeam echosounders (MBESs) and machine learning. The research integrates various scientific fields to enhance understanding of the Ku & zacute;nica Deep's underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. Advances in remote sensing, particularly, object-based image analysis (OBIA) and machine learning, have significantly improved geospatial data analysis for underwater landscapes. The study highlights the importance of using a reduced set of relevant features for training models, as identified by the Boruta algorithm, to improve accuracy and robustness. Key geomorphometric features were crucial for seafloor composition mapping, while textural features were less significant. The study found that models with fewer, carefully selected features performed better, reducing overfitting and computational complexity. The findings support hydrographic, ecological, and geological research by providing reliable seabed composition maps and enhancing decision-making and hypothesis generation.

关键词： underwater remote sensing seabed mapping multibeam echosounder object-based image analysis machine learning underwater landscape geomorphological bedforms Puck Lagoon Ku & zacute nica Deep

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Mapping waste piles in an urban environment using ground surveys, manual digitization of drone imagery, and object based image classification approach

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2025年第4期197卷 1-16页

作者： Kalonde, Patrick K. Mwapasa, Taonga Mthawanji, Rosheen Chidziwisano, Kondwani Morse, Tracy Torguson, Jeffrey S. Jones, Christopher M. Quilliam, Richard S. Feasey, Nicholas A. Henrion, Marc Y. R. Stanton, Michelle C. Blinnikov, Mikhail S. St Cloud State Univ Dept Geog & Planning 720 4th Ave South St Cloud MN 56301 USA Univ Liverpool Liverpool Sch Trop Med LIVERPOOL England Malawi Liverpool Wellcome Programme POB 30096 Blantyre Malawi Malawi Univ Business & Appl Sci Ctr Water Sanitat Hlth & Appropriate Technol Dev W Blantyre Malawi Univ Strathclyde Dept Civil & Environm Engn Glasgow Scotland Univ Stirling Fac Nat Sci Biol & Environm Sci Stirling FK9 4LA Scotland Univ St Andrews Sch Med St Andrews Scotland

There is wide recognition of the threats posed by the open dumping of waste in the environment. However, tools to surveil interventions for reducing this practice are poorly developed. This study explores the use of drone imagery for environmental surveillance. Drone images of waste piles were captured in a densely populated residential neighborhood in the Republic of Malawi. images were processed using the Structure for Motion (SfM) technique and partitioned into segments using Orfeo Toolbox mounted in QGIS software. A total of 509 segments were manually labeled to generate data for training and testing a series of classification models. Four supervised classification algorithms (Random Forest, Artificial Neural Network, Na & iuml;ve Bayes, and Support Vector Machine) were trained, and their performances were assessed regarding precision, recall, and F-1 score. Ground surveys were also conducted to map waste piles using a Global Positioning System (GPS) receiver and determine the physical composition of materials on the waste pile surface. Differences were observed between the field survey done by community-led physical mapping of waste piles and drone mapping. Drone mapping identified more waste piles than field surveys, and the spatial extent of waste piles was computed for each waste pile. The binary Support Vector Machine model predictions were the highest performing, with a precision of 0.98, recall of 0.99, and F1-score of 0.98. Drone mapping enabled the identification of waste piles in areas that cannot be accessed during ground surveys and further allowed the quantification of the total land surface area covered by waste piles. Drone imagery-based surveillance of waste piles thus has the potential to guide environmental waste policy, offer solutions for permanent monitoring, and evaluate waste reduction interventions.

关键词： Waste pile mapping object-based image analysis Orfeo Toolbox Environmental monitoring Low-income countries Waste management

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Mapping urban green structures using object-based analysis of satellite imagery: A review

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ECOLOGICAL INDICATORS 2025年 170卷

作者： Karan, Shivesh Kishore Borchsenius, Bjorn Tobias Debella-Gilo, Misganu Rizzi, Jonathan Norwegian Inst Bioecon Res Dept Geomat As Norway Norwegian Univ Life Sci Dept Geomat As Norway

Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.

关键词： Classification object-based image analysis Satellite imagery Segmentation Urban Green Structure Urban Planning

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A novel NDSM fusion approach to improve UAV-based LCC accuracy in sloping urban areas

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EUROPEAN JOURNAL OF REMOTE SENSING 2025年第1期58卷

作者： Sefercik, Umut Gunes Aydin, Ilyas Nazar, Mertcan Atalay, Can Kavzoglu, Taskin Gebze Tech Univ Dept Geomat Engn Kocaeli Turkiye Bulent Ecevit Univ Dept Geomat Engn Zonguldak Turkiye

Offering rapidly and periodically achievable and very high resolution (VHR) data at low cost, unmanned aerial vehicles (UAVs) have recently become one of the most popular remote sensing technologies. In this study, a novel Normalized Digital Surface Model (NDSM) fusion approach is proposed to improve the object-based land cover classification (LCC) performance of the RGB camera equipped UAVs. The UAV flights were completed in a multi-class sloping urban area with oblique viewing geometry and a VHR orthomosaic was generated. The object-based LCC was performed applying bottom-up (BU) multiresolution segmentation and Nearest Neighbor and Rule-based classification algorithms. In order to improve the limited segmentation performance of the RGB orthomosaic, high-quality NDSM was produced and fused as an additional imaging band. The highest LCC accuracy utilizing four imaging bands were achieved with a weight of 0.3 for NDSM and 1 for RGB bands. To preserve the information provided by the NDSM data for each image object, a single-stage segmentation process has been performed in contrast to BU segmentation. The results demonstrated that the proposed approach increased the LCC precision up to 15%, recall up to 4.71%, F1 score up to 8.58%, Kappa as 7% and overall accuracy as 4.57%.

关键词： UAV orthomosaic object-based image analysis land cover classification accuracy NDSM

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Spaciotemporal distribution characteristics of glacial lakes and the factors influencing the Southeast Tibetan Plateau from 1993 to 2023

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Yu, Mingwei Guo, Yonggang Zhang, Jian Li, Feng Su, Libin Qin, Deshun Tibet Agr & Anim Husb Univ Coll Water Conservancy & Civil Engn Nyingchi 860000 Peoples R China Res Ctr Civil Hydraul & Power Engn Tibet Nyingchi 860000 Peoples R China

The southeastern region of Tibet, which serves as the primary concentration area for marine-type glaciers, has fostered a multitude of glacial lakes that are highly sensitive to global climate change. Glacial lakes play a crucial role in regulating the freshwater ecosystems of the region, but they also pose a significant threat to local infrastructure and populations due to flooding caused by glacial lake outbursts. Currently, a limited amount of research has focused on the monitoring and analysis of glacial lakes in southeastern Tibet. Using Google Earth Engine (GEE) data in conjunction with meteorological data, this study examined the variation characteristics of glacial lakes in southeastern Tibet from 1993 to 2023. The region is subjected to segmentation and extraction of glacial lake boundaries via the FCN model. According to research findings, by 2023, southeastern Tibet is projected to contain 3,877 glacial lakes, encompassing an area of approximately 395.74 +/- 22.72 km(2). The distribution of glacial lakes in this region follows a pattern characterized by a relatively high concentration in the southern region and a relatively low concentration in the northern region. The glacial lakes with areas less than 0.1 km(2) accounted for 94.24%, whereas those exceeding 3.0 km(2) experienced the least amount of change. The analysis of regional elevation using the natural segmentation method reveals that glacial lakes are predominantly distributed within the altitude range of 4442 similar to 4909 m. The fluctuations in the annual mean precipitation and average annual temperature in the study area have decreased since 2008, while the growth trajectories of both the area and number of glacial lakes have gradually stabilized. However, given the backdrop of a slight increase in precipitation and a gradual increase in temperature, the proliferation of glacial lakes is projected to accelerate predominantly in Nyingchi City's eastern and western regions.

关键词： Southeastern Tibetan Plateau Glacial lake GEE Influencing factors object-based image analysis Spatiotemporal evolution

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Evaluating pixel-based ***-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

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Frontiers of Earth Science 2021年第1期15卷 38-53页

作者： Samira SHAYEGANPOUR Majid H.TANGESTANI Saeid HOMAYOUNI Robert K.VINCENT Department of Earth Sciences Faculty of SciencesShiraz UniversityShiraz ***Iran Centre Eau Terre Environnement Insitut National de la Recherche ScientifiqueQuebec QCJ3X1S2Canada Department of Geography Bowling Green State UniversityOhio 43403-0001USA

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared(VNIR)bands of WorldView-3(WV-3)satellite *** study area is Hormuz Island,southern Iran,a salt dome composed of dominant sedimentary and igneous *** performing the object-based image analysis(OBLA)approach,the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine(SVM)***,in the pixelbased image analysis(PBIA),the spectra of lithological end-members,extracted from imagery,were used through the spectral angle mapper(SAM)*** test samples were used in a confusion matrix to assess the accuracy of classification methods *** showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54%which was 19.33%greater than the accuracy of *** also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological *** research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 *** is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.

关键词： object-based image analysis pixel-based image analysis lithological mapping Worldview-3 Hormuz Island spectral angle mapper support vector machine

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Mapping individual tree crowns to extract morphological attributes in urban areas using unmanned aerial vehicle-based LiDAR and RGB data

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Ecological Informatics 2025年 88卷

作者： Park, Geonung Song, Bonggeun Park, Kyunghun Institute of Industrial Technology Changwon National University 20 Changwondaehak-ro Uichang-gu Gyeongsangnam-do Changwon-si 51140 South Korea ICT Research Center for Satellite Imagery Analysis Changwon National University 20 Changwondaehak-ro Uichang-gu Gyeongsangnam-do Changwon-si 51140 South Korea ENVILABS Co. Ltd. 3 Yulha-ro Gyeongsangnam-do Gimhae-si 51019 South Korea School of Smart Green Engineering Changwon National University 20 Changwondaehak-ro Uichang-gu Gyeongsangnam-do Changwon-si 51140 South Korea

Mapping individual tree crowns (ITCs) along with their morphological attributes provides foundational variables for estimating functions, such as thermal stress and carbon emissions, within the urban environment. However, to calculate morphological attributes, it is necessary to delineate ITCs, and applying the watershed segmentation (WS) algorithm, commonly used in forests, to urban environments presents challenges due to the reliance on single-band data and complexity of heterogeneous urban elements. Additionally, deep learning (DL) models, which excel in image analysis, are constrained by the labor-intensive label generation process. This study introduces a novel framework integrating machine learning (ML)- and DL-based approaches to map ITCs and extract the morphological attributes in urban areas using unmanned aerial vehicles (UAVs). Using ML-based approaches, we conducted object-based image analysis to optimize the use of the WS algorithm because applying WS directly to urban environments, as in forests, overestimated the ITC size by 151.37 %. This approach also improved the effectiveness of the DL model, Mask R-CNN, by resolving challenges in label generation. Mask R-CNN delineated ITCs with an accuracy of 0.942, suggesting its robustness in handling the heterogeneity of tree arrangements. The results demonstrate that the proposed framework is applicable for use in urban areas globally that have similar ecological conditions. ITCs with morphological attributes provide fundamental variables for evaluating ecological functions, which are scalable for improving urban environmental planning. However, UAV data may face time and cost limitations as the monitoring coverage expands, which should be considered when applying this framework. © 2025 The Authors

关键词： Individual tree crown delineation Instance segmentation object-based image analysis Tree morphological attributes UAVs Watershed segmentation

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Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces

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Journal of Environmental Sciences 2011年第8期23卷 1375-1383页

作者： Weifeng Li Zhiyun Ouyang Weiqi Zhou Qiuwen Chen State Key laboratory of Urban and Region Ecology Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences Beijing1000~85 China. Department of Plant Sciences University of California Davis Mail Stop 1 1210 PES One Shields Ave. Davis CA 95616 USA

Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.

关键词： remote sensing impervious surface landscape pattern spatial resolution object-based image analysis urban landscape

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Detecting olive quick decline syndrome: A satellite-based dataset for a case study in Apulia Region

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Data in Brief 2025年 60卷

作者： Lorenzo Crecco Danilo Morelli Elisabetta Raparelli Mara Di Giulio Sofia Bajocco

The bacterium Xylella fastidiosa (Xf) is a plant pathogen first identified in Europe in 2013, specifically in olive groves in the Apulia region (south-eastern Italy). It is now spreading across the Mediterranean basin and poses a serious threat to the local economy by causing branch desiccation and the rapid death of olive trees, a condition known as olive quick decline syndrome (OQDS). Several studies have investigated the potential of remote sensing (RS) technology to monitor OQDS over time and space; however, accurate and reliable data on OQDS occurrence remain scarce. To enhance the distribution data of Xf-infected trees in the Apulia region, we investigated an infection hotspot of 25 km² area in the province of Brindisi, where records of infections were documented in 2019 and 2020. Three very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees. Through visual interpretation, 2340 trees were identified most likely as either infected or removed due to OQDS. This dataset provides a valuable resource for developing or validating RS techniques for early detection of OQDS. Furthermore, it could support studies aimed to evaluate spectral bands or indices most correlated with infection presence. Finally, the dataset can be integrated with other Xf-infection presence data to support species distribution model studies.

关键词： Xylella fastidiosa OQDS WorldView-2 object-based image analysis Visual interpretation

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