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geographic object-based image analysis for landslide identification using machine learning on google earth engine

ENVIRONMENTAL EARTH SCIENCES 2025年第3期84卷 1-27页

作者： Khadka, Diwakar Zhang, Jie Sharma, Atma Tongji Univ Dept Geotech Engn 1239 Siping Rd Shanghai 200092 Peoples R China Tongji Univ Dept Geotech Engn Key Lab Geotech & Underground Engn Minist Educ 1239 Siping Rd Shanghai 200092 Peoples R China

Landslides significantly threaten human life and infrastructure, requiring accurate and timely identification for effective hazard assessment and management. This study proposes a new approach combining geographic object-based image analysis (GEOBIA) and machine learning on the Google Earth Engine (GEE) platform, utilizing high-resolution Sentinel-2 imagery and NASADEM data. Our methodology begins with Simple Non-iterative Clustering (SNIC) segmentation, which divides the images into homogeneous super-pixels. This step is crucial for reducing 'salt and pepper' noise and enhances the differentiation of spectrally similar objects through advanced texture, shape, and contextual analysis. Following segmentation, Gray Level Co-occurrence Matrix (GLCM) feature extraction is employed to gather critical textural information, which is pivotal in discerning surface roughness, heterogeneity, and composition-key factors in identifying landslide-prone areas. To manage the high dimensionality of the data, Principal Component analysis (PCA) is utilized for dimensionality reduction, transforming original variables into a set of uncorrelated principal components that facilitate more efficient subsequent analysis. Various machine learning algorithms are utilized, including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). We use the GEE platform to leverage extensive geospatial data and computational power. The performance of SVM, RF, and CART algorithms is evaluated for landslide detection. RF demonstrates superior accuracy in detecting landslides, achieving an overall accuracy of 87.41%, surpassing SVM (85.47%) and CART (68.45%). Integrating SNIC segmentation, GLCM feature extraction, PCA analysis, and RF algorithm within the GEOBIA framework using the GEE platform shows promising results for improving landslide identification, monitoring, and risk assessment.

关键词： Landslide identification geographic object-based image analysis Machine Learning Google Earth Engine

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Mapping alluvial mine dynamics in the Atewa landscape in Ghana using geographic object-based image analysis (GEOBIA) and GIS

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2025年第4期197卷 1-20页

作者： Adams, Lukman Boakye Hayakawa, Yuichi S. Hokkaido Univ Grad Sch Environm Sci Sapporo Hokkaido 060810 Japan Hokkaido Univ Fac Environm Earth Sci Sapporo Hokkaido 060810 Japan

The Atewa Range Forest Reserve (ARFR) in Ghana, a key biodiversity hotspot, is increasingly impacted by alluvial gold mining, which has been expanding in the region. However, limited research has focused on mining activities within the forest reserve or their proximity to river bodies. This study used high-resolution PlanetScope satellite imagery (2018-2023) and geographic object-based image analysis (GEOBIA) to monitor mining activities and land use changes in the Atewa landscape. Post-classification change detection was used to determine land use land cover (LULC) classes that transitioned to alluvial mines while assessing post-mine vegetation recovery. Buffer analysis was used to examine the distance between mines and the forest reserve as well as water channels. Findings revealed an annual increase in mining activities at a change rate of 12.3% between 2018 and 2023, with significant vegetation conversion in the Atewa landscape. Mining exhibited a more irregular pattern in the forest reserve, with a rate of change of - 21.4%. The buffer analysis showed that a significant portion of mining occurred within 100 m of river channels, with at least 49.7% of the mined area annually falling within this zone. The buffer analysis also indicated that at least 0.7% of mining activities in the landscape were within the 100 m buffer around the ARFR. The study highlights the increasing threat of mining to the ARFR and the importance of monitoring these impacts on the forest and surrounding ecosystems.

关键词： Remote sensing GIS geographic object-based image analysis Mining Forest reserve

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geographic object-based image analysis: A Primer and Future Directions

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REMOTE SENSING 2020年第12期12卷 2012-2012页

作者： Kucharczyk, Maja Hay, Geoffrey J. Ghaffarian, Salar Hugenholtz, Chris H. Univ Calgary Dept Geog Calgary AB T2N 1N4 Canada

geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial);(ii) best practices informed by research;and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.

关键词： geographic object-based image analysis GEOBIA object-based image analysis OBIA machine learning deep learning convolutional neural network CNN GEOCNN

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Effect of image-Processing Routines on geographic object-based image analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas

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REMOTE SENSING 2022年第17期14卷 4403页

作者： Jawak, Shridhar D. Wankhede, Sagar F. Luis, Alvarinho J. Balakrishna, Keshava Svalbard Sci Ctr SIOS Knowledge Ctr Svalbard Integrated Arctic Earth Observing Syst S POB 156 N-9171 Longyearbyen Svalbard Norway Manipal Acad Higher Educ Manipal Inst Technol Dept Civil Engn Manipal India Govt India Earth Syst Sci Org Natl Ctr Polar & Ocean Res NCPOR Minist Earth Sci Vasco Da Gama 403804 Goa India

Advancements in remote sensing have led to the development of geographic object-based image analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram-Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-angstrom lesund, Svalbard, and Chandra-Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subse

关键词： geographic object-based image analysis atmospheric correction pansharpening WorldView-2 Ny-angstrom lesund Chandra-Bhaga basin glacier surface facies

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A FRAMEWORK FOR geographic object-based image analysis (GEOBIA) based ON geographic ONTOLOGY

A FRAMEWORK FOR GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS (GEOB...

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International Workshop on image and Data Fusion (IWIDF)

作者： Gu, H. Y. Li, H. T. Yan, L. Lu, X. J. Chinese Acad Surveying & Mapping Key Lab Geoinformat State Bur Surveying & Mapping 28 Lianhuachi Rd Beijing Peoples R China Wuhan Univ Sch Geodesy & Geomat Luojiashan Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100864 Peoples R China

ISBN: (纸本)9781629935201

GEOBIA (geographic object-based image analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience. The lack of a systematic approach designed to conceptualize and formalize the class definitions makes GEOBIA a highly subjective and difficult method to reproduce. This paper aims to put forward a framework for GEOBIA based on geographic ontology theory, which could implement "geographic entities - image objects - geographic objects" true reappearance. It consists of three steps, first, geographical entities are described by geographic ontology, second, semantic network model is built based on OWL(ontology web language), at last, geographical objects are classified with decision rule or other classifiers. A case study of farmland ontology was conducted for describing the framework. The strength of this framework is that it provides interpretation strategies and global framework for GEOBIA with the property of objective, overall, universal, universality, etc., which avoids inconsistencies caused by different experts' experience and provides an objective model for mage analysis.

关键词： geographic object-based image analysis geographic Ontology Semantic Network Model Ontology Web Language

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AN INTELLIGENT VECTOR AGENT PROCESSING UNIT FOR geographic object-based image analysis

AN INTELLIGENT VECTOR AGENT PROCESSING UNIT FOR GEOGRAPHIC O...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Borna, Kambiz Sirguey, Pascal Moore, Antoni B. Univ Otago Sch Surveying POB 56 Dunedin New Zealand

ISBN: (纸本)9781479979295

The consideration of meaningful geographical objects instead of individual pixels is the main distinguishing characteristic of geographic object-based image analysis (GEOBIA) methods. In this regard, meaningful geographical objects enable image analysis methods to consider extra information, such as geometry, texture and spatial relationships to analyse imagery. However, meaningful geographical objects are determined with only limited spatial knowledge of real world objects. Furthermore, such objects remain associated with a static geometry that depend greatly on user-defined parameters such as scale and colour during segmentation. This paper explores geographical Vector Agents (GVAs) as an automated processing unit to address the above-mentioned limitations of the image objects, allowing them to evolve in terms of geometry and attributes during a dynamic and concurrent segmentation/classification process. Experimental tests indicate satisfactory results of the capabilities of GVAs for generic applicability in GEOBIA methods.

关键词： geographic object-based image analysis GEOBIA geographical Vector Agents GVA

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AN INTELLIGENT VECTOR AGENT PROCESSING UNIT FOR geographic object-based image analysis

AN INTELLIGENT VECTOR AGENT PROCESSING UNIT FOR GEOGRAPHIC O...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Kambiz. Borna Pascal. Sirguey Antoni. B. Moore School of Surveying University of Otago

ISBN: (纸本)9781479979301

关键词： geographic object-based image analysis GEOBIA geographical Vector Agents GVA

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Comparing multispectral and hyperspectral UAV data for detecting peatland vegetation patterns

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 132卷

作者： Pang, Yuwen Rasanen, Aleksi Wolff, Franziska Tahvanainen, Teemu Mannikko, Milja Aurela, Mika Korpelainen, Pasi Kumpula, Timo Virtanen, Tarmo Univ Helsinki Fac Biol & Environm Sci Ecosyst & Environm Res Program Environm Change Res Unit ECRU Helsinki Finland Nat Resources Inst Finland Luke Oulu Finland Univ Oulu Geog Res Unit Oulu Finland Univ Eastern Finland Dept Geog & Hist Studies Joensuu Finland Univ Eastern Finland Dept Environm & Biol Sci Joensuu Finland Finnish Meteorol Inst Helsinki Finland

Northern peatland vegetation exhibits fine-scale spatial and spectral heterogeneity that can potentially be captured with uncrewed aerial vehicle (UAV) data due to their ultra-high spatial resolution (<10 cm). From this perspective, the contribution of different spectral sensors in mapping various vegetation characteristics has not been thoroughly investigated. We delineated spatial patterns of plant community clusters, plant functional types (PFTs), and selected plant species with UAV hyperspectral (HS), UAV multispectral (MS), and airborne LiDAR (light detection and ranging) topography (TP) data in two northern peatlands. We conducted random forest (RF) regressions in a geographic object-based image analysis (GEOBIA) framework and compared the relative contributions of the different datasets. In the best regression models, the percentage of explained variance was 24-74 % (RMSE:0.24-0.31), 40-90 % (RMSE:0.12-0.41), and 18-90 % (RMSE:0.03-0.40) for plant community clusters, PFTs, and plant species, respectively. The MS-TP combination had, in many cases, the highest performance, while HS-based models had better performance for some plant community clusters, PFTs, and plant species. TP features were useful only in certain situations. Overall, our results suggest that UAV MS imagery combined with TP data outperformed or performed at least almost as well as the models using UAV HS data and while all data combinations are capable for fine-scale detection of vegetation in northern peatlands. A more comprehensive investigations of data processing and methodology selection is needed to conclude if there is an added value of UAV HS data for peatland vegetation monitoring.

关键词： Peatland vegetation mapping Hyperspectral remote sensing geographic object-based image analysis Random forest

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Center pivot field delineation and mapping: A satellite-driven object-based image analysis approach for national scale accounting

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 175卷 1-19页

作者： Johansen, Kasper Lopez, Oliver Tu, Yu-Hsuan Li, Ting McCabe, Matthew Francis King Abdullah Univ Sci & Technol KAUST Water Desalinat & Reuse Ctr Div Biol & Environm Sci & Engn Hydrol Agr & Land Observat Grp Thuwal 239556900 Saudi Arabia

Center pivot irrigation systems are used to enhance crop production in many countries around the world. Establishing the location and extent of such fields provides information that assists in describing a range of agricultural metrics, including crop identification, yield forecasts, monitoring of irrigation requirements and crop water use, as well as supporting national and regional auditing, licensing and compliance efforts. However, detailed information on the number, extent and changing dynamics of agricultural fields is often lacking in many countries: nowhere more so than in developing regions. To address this lack, we performed a national scale accounting of center pivot fields in Saudi Arabia, using a three year multi-temporal analysis of Landsat-8 satellite data. A geographic object-based image analysis approach was developed based on five 50 x 50 km sub-areas extracted from Landsat data for the year 2015, and applied to delineate individual center pivot fields at a national scale for 2013, 2014 and 2015. The extent of fields was determined via a map of the annual maximum Normalized Difference Vegetation Index (NDVI), while a 15 m spatial resolution map of annual panchromatic band minimums was used to produce an edge detection layer to delineate individual adjoining fields. Amongst a range of classification parameters that were included in the object-based mapping approach, shape information, such as the center pivot field length, length:width ratio, and elliptic fit, were identified as critical parameters. Applying the rule-set that was developed from the five 50 x 50 km sub-regions to the national scale resulted in the identification of 36,052 (11,103 km(2)), 38,114 (11,902 km(2)), and 37,254 (11,555 km(2)) individual fields in 2013, 2014, and 2015, respectively. Approximately 94% of these fields were correctly detected, while their individually measured area was mapped with >95% combined accuracy for fields >0.225 km(2) when evaluated against manually de

关键词： geographic object-based image analysis Agricultural center pivot fields Landsat Time-series NDVI Edge detection

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A new framework for GEOBIA: accurate individual plant extraction and detection using high-resolution RGB data from UAVs

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International Journal of Digital Earth 2023年第1期16卷 2599-2622页

作者： Kaile Yang Zhangxi Ye Huan Liu Xiaoyu Su Chenhui Yu Houxi Zhang Riwen Lai College of Forestry Fujian Agriculture and Forestry UniversityFuzhou350028People’s Republic of China University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province No.15 Shangxiadian RoadFuzhou350002FujianPeople’s Republic of China Key Laboratory of State Forestry and Grassland Administration on Soil and Water Conservation of Red Soil Region in Southern China Fuzhou350002People’s Republic of China Cross-Strait Collaborative Innovation Center of Soil and Water Conservation Fuzhou350002People’s Republic of China National Positioning Observation and Research Station of Red Soil Hill Ecosystem in Changting Fuzhou350002People’s Republic of China

Citrus(Citrus reticulata),which is an important economic crop worldwide,is often managed in a labor-intensive and inefficient manner in developing countries,thereby necessitating more rapid and accurate alternatives tofield surveys for improved crop *** this study,we propose a novel method for individual tree segmentation from unmanned aerial vehicle remote sensing(RS)using a combination of geographic object-based image analysis(GEOBIA)and layer-adaptive Euclidean distance transformation-based watershed segmentation(LAEDT-WS).First,we use a GEOBIA support vector machine classifier that is optimized for features and parameters to identify the boundaries of citrus tree canopies accurately by generating mask ***,our LAEDT workflow separates connected canopies and facilitates the accurate segmentation of individual canopies using *** method exhibited an F1-score improvement of 10.75%compared to the traditional WS method based on the canopy height ***,it achieved 0.01%and 1.38%higher F1-scores than the state-of-the-art deep learning detection networks YOLOX and YOLACT,respectively,on the test *** method can be extended to detect larger-scale or more complex structured crops or economic plants by introducing morefinely detailed and transferable RS images,such as high-resolution or LiDAR-derived images,to improve the mask base map.

关键词： Crop management unmanned aerial vehicle remote sensing watershed segmentation geographic object-based image analysis

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