Harnessing Deep Learning for Precision Tree Extraction in ArcGIS Pro

Muhammad Aleem; Ehtesham Ali; Mohd Abdullah Al Mamun; Jalal Uddin Md Akbar; Aqsa Saleem; Hassan Ibrahim; Abdullah Khan

No Detail Found!

Harnessing Deep Learning for Precision Tree Extraction in ArcGIS Pro

Author(s)

Muhammad Aleem Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah 26600 Pekan,Pahang,Malaysia

Ehtesham Ali Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al Sultan Abdullah,26600, Pekan,Malaysia

Mohd Abdullah Al Mamun MBA in Information Technology Management Westcliff University,,USA

Jalal Uddin Md Akbar Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah 26600 Pekan,Pahang,Malaysia

Aqsa Saleem Department of Computer Science, COMSATS University Islamabad - Sahiwal Pakistan,,

Hassan Ibrahim Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah 26600 Pekan,Pahang,Malaysia

Abdullah Khan Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology,,

Abstract

The fields of environmental management and geographic analysis have been completely transformed by the incorporation of deep learning techniques with Geographic Information Systems (GIS). In this thesis, ArcGIS Pro is used to investigate the use of deep learning techniques, specifically the Single Shot MultiBox Detector (SSD), for tree extraction in urban settings. The focus of the assessment of the SSD model's accuracy and dependability in tree identification from aerial photography is the study region of Pekan, Malaysia. cc Analyses conducted in comparison with alternative object identification algorithms, such as FCN and R-CNN, demonstrate how much faster, more accurate, and more efficient SSD is. The findings show that the SSD model can effectively identify trees while reducing false positives and false negatives, with high precision and recall rates reaching over 90%. The model's efficacy is further supported by the IoU metric, which shows a noteworthy degree of spatial alignment and precision between predicted and ground truth bounding boxes. Visual interpretations give specific instances of how accurately SSD can identify trees; these include bounding box visualisations and confusion matrices. Enhancing dataset augmentation and diversity, connecting SSD with GIS platforms for smooth data analysis, and offering user training and capacity-building activities are among the recommendations for future research and development. Sustainable urban development methods and automated tree recognition systems require cooperation and knowledge exchange among researchers, practitioners, and policymakers. Together deep learning and ArcGIS Pro integration provides a strong toolkit for environmental monitoring and tree extraction in urban settings like Pekan, Malaysia. With its exceptional precision, efficacy, and adaptability, the SSD model is a mainstay of automated tree recognition systems, opening the door to well-informed choices and environmentally sound practices

Publication Details

Page(s) 1439-1453

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 4, Year: 2024

Request the full text from PASTIC

Keywords

Deep learning, Aerial Photography, Precision Tree Extraction, ArcGIS Pro, Single Shot MultiBox Detector (SSD), Urban Tree Identification

References

Abdilah Y.,Extraction Building .2020 .Esri, the science of Where, Deep Learning for Tree Counting. , : .

Davis D. S.,Lundin J. .2021 .Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms. , : .

Oppong J. .2023 .November 7). Extracting Building Footprints and Trees from Imagery Using ArcGIS Pro. , : .

Hennig S. .2021 .Orchard Meadows: Opportunities and Challenges of Tree Detection Using ArcGIS Pro Deep Learning Tools. , : .

Pleșoianu A.-I.,Stupariu S.,Șandric I. .2020 .Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model. , : .

Chakraborty S. D. .2024 .Tree Canopy Extraction Using Segmentation Process in ArcGIS Pro. , : .

Paul A. V. .2020 .SSD Object Detection in Real Time (Deep Learning and Caffe. , : .

Jayaraman K. (n.d.). . .Using Machine Learning & Deep Learning With ArcGIS Imagery. , : .

Fulham G. R.,Farmer A.,T. G. A. .2023 .Machine Learning in Urban Tree Canopy Mapping: A Columbia, SC Case Study for Urban Heat Island Analysis. , : .

.2022 .Deep Learning Applied to Object Detection with ArcGIS Pro. , : .

Fulham G. R.,Farmer A.,T. G. A. .2023 .Machine Learning in Urban Tree Canopy Mapping: A Columbia, SC Case Study for Urban Heat Island Analysis. , : .

Pirasteh S.,Rashidi P.,Rashidi P.,Rastiveis H. .2019 .Developing an Algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images. , : .

Liu Y.,Wei H.,Han Z. .2020 .. Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks., : .

Abd-Elrahman A.,Britt K.,Liu T. .2021 .Deep Learning Classification of HighResolution Drone Images Using the ArcGIS Pro Software. , : .

Alison .2022 .ArcGIS: Learn Deep Learning in ArcGIS to Advance GIS Skills. , 11 : .

Pirasteh S.,Rashidi P.,Rastiveis H. .2019 .Developing an Algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images. , : .

Bakhit .2024 ."IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster. " Journal of Advanced Research in Applied Mechanics 117.1, : 137-149.

Hoummaidi L. E.,Larabi A.,Alam K. .2021 .Using Unmanned Aerial Systems and Deep Learning for Agriculture Mapping in Dubai. , : .

Hennig S. .2021 .Orchard Meadow Trees: Tree Detection Using Deep Learning in ArcGIS Pro. , : .

Nicholson E.,Perry J. J.,Dean J. R. .2024 .Use of Remote Sensing and Image Processing for Identification of Wild Orchids. , : .

Zhang Y.,Tan Y.,Onda Y.,Hashimoto A.,Gomi T.,Chiu C.,Inokoshi S. .2023 .A Tree Detection Method Based on Trunk Point Cloud Section in Dense Plantation Forest Using Drone LiDAR Data. , : .

Lee D. H.,Park H. Y.,Lee J. .2024 .A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement. PUBMED Central, : .

Basheer S.,Wang X.,Farooque A. A.,Nawaz R. A. .2022 .October 6). Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques. , : .

Paul A. V. .2020 .SSD Object Detection in Real Time (Deep Learning and Caffe). , : .

Silva D. Q.,Santos F. N. d.,Sousa A. J.,Filipe V. .2021 .Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics. , : .

Liu W.,Anguelov D.,Erhan D.,Szegedy C.,Reed S.,Fu S.,Berg A. C. .2022 .SSD: Single Shot MultiBox Detector. , : .

Zheng Y.,Wu G. .2021 .Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location With High-Resolution Remote Sensing Imagery. , : .

Lassalle G.,Ferreira M. P.,de Souza Filho L. E.,C. R. L. E. .2022 .Deep learningbased individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 189 : .

Paul A. V. .2020 .SSD Object Detection in Real Time (Deep Learning and Caffe. , : .

Beloiu M.,Heinzmann L.,Rehush N.,Gessler A.,Griess V. C. .2023 .Individual TreeCrown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning. , : .

Öçer N. E.,Kaplan G.,Erdem F.,Matci D. K. .2020 .Tree Extraction from Multi-Scale UAV Images Using Mask R-CNN with FPN. , : .

Khalid N.,Shahrol N. A.,Science Environmental .2022 .Evaluation of the Accuracy of Oil Palm Tree Detection Using Deep Learning and Support Vector Machine Classifiers. , : .

Vivian A. P. .2020 .. , : .

Hennig S. .2021 .Orchard Meadow Trees: Tree Detection Using Deep Learning in ArcGIS Pro. , : .

Hartling S.,Sagan V.,Sidike P.,Maimaitijiang M.,Carron J. .2019 .Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning. , : .

Şenol İbrahim,Yiğit H. & Yasin,A. H. & Yasin .2023 .Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery. Turkish Journal of Geographic Information Systems.[cross ref], : .

Liu Y.,Wei H.,Han Z. .2020 .. Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks., : .

Pleșoianu A.-I.,Stupariu S.,Șandric I. .2020 .Individual Tree-Crown Detection and Species Classification in Very High-Resolution RemoteSensing Imagery Using a Deep Learning Ensemble Model. , : .

Hennig S. .2021 .Orchard Meadow Trees: Tree Detection Using Deep Learning in ArcGIS Pro. , : .

Langning H.,Eva L.,Johan H. .2022 .A New Method for Tree Crown Segmentation from LiDAR Data Based on a Symmetrical Structure Detection Algorithm (SSD).. , : .

Davis D. S.,Lundin J. .2021 .Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms. , : .

Öçer N. E.,Kaplan G.,Erdem F.,Matci D. K. .2020 .Tree extraction from multi-scale UAV images using Mask R-CNN with FPN. , : .

Senol H. İ.,Yiğit A. Y. .2023 .Extracting Building Footprints and Trees from Imagery Using ArcGIS Pro. Turkish Journal of Geographic Information Systems., : .

Khalid N.,Shahrol N. A. .2022 .Evaluation of the Accuracy of Oil Palm Tree Detection Using Deep Learning and Support Vector Machine Classifiers. , : .

Zhang Y.,Tan Y.,Onda Y.,Hashimoto A.,Gomi T.,Chiu C.,Inokoshi S. .2023 .A Tree Detection Method Based on Trunk Point Cloud Section in Dense Plantation Forest Using Drone LiDAR Data. , : .

Wijaya D.,Seto I.,Aditama A.,Austen S. A. .2024 .Tree Counting with Deep Forest Algorithm for Kulon Progo District in Yogyakarta, Indonesia Using Pleiades Satellite Imagery. , : .

Khalid N.,Shahrol N. A.,Science Environmental .2022 .Evaluation of the Accuracy of Oil Palm Tree Detection Using Deep Learning and Support Vector Machine Classifiers. , : .

Fulham G. R.,Farmer A.,T. G. A. .2023 .Machine Learning in Urban Tree Canopy Mapping: A Columbia, SC Case Study for Urban Heat Island Analysis. , : .

Chafiq T.,Hachimi H.,Raji M.,Zerraf S. .2021 .U-Net: Deep Learning for Extracting Building Boundary Collected by Drone of Agadir's Harbor. Published on June 26, : .

Chafiq T.,Hachimi H.,Raji M.,Zerraf S. .2021 .Deep Learning for Extracting Building Boundaries Collected by Drone of Agadir's Harbor.. , : .

Davis D. S.,Lundin J. .2021 .Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms. Published on September 15, : .

Basheer S.,Wang X.,Farooque A. A.,Nawaz R. A. .2022 .Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques. , : .

Hartling S.,Sagan V.,Sidike P.,Maimaitijiang M.,Carron J. .2019 .Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning. , : .

Arnold C. R. .2023 .Locating Low Head Dams Using a Deep Learning Model in ArcGIS Pro with Aerial Imagery. , : .

Silva D. Q.,Santos F. N. d.,Sousa A. J.,Filipe V. .2021 .Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics. , : .

Hartling S.,Sagan V.,Sidike P.,Maimaitijiang M.,Carron J. .2019 .Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning. , : .

Wang Y.,Yang L.,Liu X.,Yan P. .2024 .An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+.. , : .

Zheng Y.,Wu G. .2021 .Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location with High-Resolution Remote Sensing Imagery. , : .

Hennig S. .2021 .. Orchard Meadow Trees: Tree Detection Using Deep, : .

Hong X. .2021 .August). A Convolutional Neural Network for Detecting and Mapping Built Environment at Neighborhood Scale. , : .

Zheng Y.,Wu G. .2021 .Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location With High-Resolution Remote Sensing Imagery. , : .

Caceres C. .2022 .. Automated Semantic Segmentation of Aerial Imagery using Synthetic Data., : .

Pleșoianu A.-I.,Stupariu S.,Șandric I. .2020 .Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model. , : .

Zheng Y.,Wu G. .2021 .Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location With High-Resolution Remote Sensing Imagery. , : .

Huo L.,Lindberg E.,Holmgren J. .2022 .A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD). , : .

Oppong J. .2023 .Extracting Building Footprints and Trees from Imagery using ArcGIS Pro. , : .

Queirós da Silva ,Santos F. N.,Sousa A. J.,Filipe V. .2021 .Visible and Thermal ImageBased Trunk Detection with Deep Learning for Forestry Mobile Robotics. , : .

Abdilah Y. .2021 .Tree Counting, and Advanced Tree Analysis with Deep Learning. , : .

Zhang Y.,Tan Y.,Onda Y.,Hashimoto A.,Gomi T.,Chiu C.,Inokoshi S. .2023 .A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data. , : .

Nurkarim W.,Wijayanto A. W. .2022 .Building Footprint Extraction and Counting on Very High-Resolution Satellite Imagery using Object Detection Deep Learning Framework. , : .

Li Y.,Wei H.,Han Z. .2020 .. Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks.[cross ref], : .

Moharram D.,Yuan X.,Li D. .2023 .Tree Seedlings Detection and Counting Using a Deep Learning Algorithm. , : .

MathuraBai B.,Maddali V. P.,Devineni C.,Bhukya I.,Bandari S. .2022 .Object Detection using SSD-MobileNet. International Research Journal of Engineering and Technology (IRJET), : .

Sun Y.,Li Z.,He H.,Guo L.,Zhang X.,Xin Q. .2022 .Counting trees in a subtropical mega city using the instance segmentation method. International Journal of Applied Earth Observation and Geoinformation, : .

Pandey A.,Mishra A. .2016 .Building Footprint Extraction and Counting on Very HighResolution Satellite Imagery using Object Detection Deep Learning Framework. , : .

Yu K.,Hao Z. .2022 .Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery. , : .

Chen G.,Shang Y. .2022 .Transformer for Tree Counting in Aerial Images. , : .

Hao Z.,Post C. .2022 .How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation. , : .

Yao L.,Liu T.,Qin J.,Lu N.,Zhou C. .2021 .Tree counting with high spatial-resolution satellite imagery based on deep neural networks. , : .

.2024 .. , : .

Akbulut Z.,Özdemir S.,Acar H.,Karsli F. .2018 .Automatic Building Extraction from Image and LiDAR Data with Active Contour Segmentation. , : .

Xiao C.,Qin R.,Huang X. .2018 .September). A Study of Using Fully Convolutional Network for Treetop Detection on Remote Sensing Data. , : .

Vigneshwaran S.,Tamburi V. N. .2023 .Identification of coconut palm trees using single shot detector deep learning model.. , : .

Liu T.,Abd-Elrahman A. .2018 .An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System. , : .

Li S.,Brandt M.,Fensholt R.,Kariryaa A. .2023 .Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale. , : .

Hariadi B. .2023 .Oil Palm Tree Detection Using ArcGIS Deep Learning Tools on Aerial Imageries Obtained from Unmanned Aerial Vehicle. , : .

Khalid N.,Shahrol N. A. .2022 .Evaluation The Accuracy of Oil Palm Tree Detection Using Deep Learning and Support Vector Machine Classifiers. , : .

Wang Y.,Yang L.,Liu X.,Yan P. .2024 .An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+. , : .

Xiao C.,Qin R.,Huang X. .2018 .A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD).. , : .

Hong X. .2021 .August). A convolutional neural network for detecting and mapping-built environment at neighborhood scale. , : .

Silva D. Q. da,Santos F. N. dos,Filipe A. J.,V. A. J. .2021 .Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics. , : .

Caceres C. .2022 .. Automated semantic segmentation of aerial imagery using synthetic data. [cross ref], : .

Oppong J. .2023 .Extracting building footprints and trees from imagery using ArcGIS Pro. , : .

Şenol İbrahim,Yiğit H.,A. Y. H. .2023 .DEEP LEARNING BASED INDIVIDUAL TREE CROWN DELINEATION IN MANGROVE FORESTS USING VERY-HIGH-RESOLUTION SATELLITE IMAGERY. , : .

Abdilah Y. .2021 .August 2). Tree counting, and advanced tree analysis with deep learning. [cross ref], : .

Nurkarim W.,Wijayanto A. W. .2022 .Building footprint extraction and counting on very high-resolution satellite imagery using object detection deep learning framework. , : .

Öçer N. E.,Kaplan G.,Erdem F.,Matci D. K. .2020 .Tree extraction from multi-scale UAV images using Mask R-CNN with FPN. , : .

Moharram D.,Yuan X.,Li D. .2023 .January 9). Tree seedlings detection and counting using a deep learning algorithm. , : .

Pandey A.,Mishra A. .2016 .Building detection and extraction techniques: A review. , : .

Chen G.,Shang Y. .2022 .Transformer for tree counting in aerial images. , : .

Yao L.,Liu T.,Qin J.,Lu N.,Zhou C. .2021 .Tree counting with high spatialresolution satellite imagery based on deep neural networks. [cross ref], : .

Akbulut Z.,Özdemir S.,Acar H.,Karsli F. .2018 .Automatic building extraction from image and LiDAR data with active contour segmentation. , : .

Hani ,Munir S.,Younis S.,Saeed T.,Younis H. .2023 .Automatic tree counting from satellite imagery using YOLO V5, SSD, and UNET models: A case study of a campus in Islamabad, Pakistan. , : .

Moharram D.,Yuan X.,Li D. .2023 .January 9). Tree seedlings detection and counting using a deep learning algorithm. , : .

Aleem .2024 .. Noorhuzaimi Mohd Noor. "Predicting Android App Success Before Google Play Store Launch Using Machine Learning." Library Progress International 44.3, : 1907-1918.

Vigneshwaran S.,Tamburi V. N. .2023 .Identification of coconut palm trees using single shot detector deep learning model. , : .

Li S.,Brandt M.,Fensholt R.,Kariryaa A. .2023 .Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale. , : .

Citations

Downloads

Views