Deep Learning for High-Resolution Geospatial Image Analysis

Authors: Kirti Vasdev

DOI: https://doi.org/10.5281/zenodo.14535586

Short DOI: https://doi.org/g8wjp2

Country: USA

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Abstract: Deep learning has emerged as a revolutionary approach for high-resolution geospatial image analysis, offering unprecedented capabilities in feature extraction, classification, segmentation, and change detection. This paper explores the application of deep learning techniques to geospatial data, focusing on methodologies, public datasets, challenges, and potential solutions. Furthermore, it provides a detailed workflow for implementing deep learning in geospatial image analysis and discusses advancements in leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers for spatial applications. Key use cases in urban planning, environmental monitoring, and disaster response are highlighted, alongside a summary of publicly available datasets.

Keywords: Deep Learning, Geospatial Analysis, High-Resolution Images, Convolutional Neural Networks, Geospatial Datasets, Remote Sensing, Environmental Monitoring

Paper Id: 231874

Published On: 2024-11-11

Published In: Volume 12, Issue 6, November-December 2024

Cite This: Deep Learning for High-Resolution Geospatial Image Analysis - Kirti Vasdev - IJIRMPS Volume 12, Issue 6, November-December 2024. DOI 10.5281/zenodo.14535586

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