Optimization of Geo-spatial Object Segmentation for High-Density Places Using Spatial Techniques

DOI

10.2991/978-94-6463-858-5_279How to use a DOI?

Keywords

CNN; Machine Learning; Deep Learning; ResNet50; EfficientNetV2

Abstract

The remote sensing imagery classification is a vital application of machine learning technology going beyond satellite-based platforms into aerial imagery. These techniques substitute for conventional manual categorization, allowing for auto- mated detection of particular land features in geospatial images. Extraction and categorization of geospatial features, e.g., gravel deposits for construction, water bodies, vegetation, and urban buildings, are vital for numerous applications, e.g., environmental surveillance, urban planning, and disaster relief. Geospatial image analysis also facilitates the management of resources such as water and air quality, traffic control optimization, and enhanced post-disaster relief operations. Convolutional Neural Networks (CNNs), which fall under the broad category of deep learning, take a central part in feature extraction and classification with minimal need for human intervention. Advanced models such as ResNet50 and EfficientNetV2 improve the accuracy and integrity of image classification by learning sophisticated patterns from large data. Their use guarantees better decision-making in most geospatial applications, culminating in efficient and timely analysis.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Y. Divya
AU  - M. Shanmuga Sundari
AU  - Dugyala Ansika
AU  - Challa Pranavi
AU  - Kommu Sankruthi
PY  - 2025
DA  - 2025/11/04
TI  - Optimization of Geo-spatial Object Segmentation for High-Density Places Using Spatial Techniques
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3343
EP  - 3350
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_279
DO  - 10.2991/978-94-6463-858-5_279
ID  - Divya2025
ER  -