# Fusion of Deep Learning and GIS for Advanced Remote Sensing Image Analysis

arXiv: https://arxiv.org/abs/2412.19856 · HTML: https://mlsanity.com/paper/2412.19856/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [A New Mask R-CNN Based Method for Improved Landslide Detection](https://mlsanity.com/paper/2010.01499/) (2020)
- [SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction](https://mlsanity.com/paper/2308.03586/) (2023)
- [A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids](https://mlsanity.com/paper/1803.03320/) (2018)
- [AI-based Optimal scheduling of Renewable AC Microgrids with bidirectional LSTM-Based Wind Power Forecasting](https://mlsanity.com/paper/2208.04156/) (2022)

## Abstract (arXiv)

This paper presents an innovative framework for remote sensing image analysis by fusing deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, with Geographic Information Systems (GIS). The primary objective is to enhance the accuracy and efficiency of spatial data analysis by overcoming challenges associated with high dimensionality, complex patterns, and temporal data processing. We implemented optimization algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), to fine-tune model parameters, resulting in improved performance metrics. Our findings reveal a significant increase in classification accuracy from 78% to 92% and a reduction in prediction error from 12% to 6% after optimization. Additionally, the temporal accuracy of the models improved from 75% to 88%, showcasing the frameworks capability to monitor dynamic changes effectively. The integration of GIS not only enriched the spatial analysis but also facilitated a deeper understanding of the relationships between geographical features. This research demonstrates that combining advanced deep learning methods with GIS and optimization strategies can significantly advance remote sensing applications, paving the way for future developments in environmental monitoring, urban planning, and resource management.