Novel Encoder–Decoder Architecture with Attention Mechanisms for Satellite-Based Environmental Forecasting in Smart City Applications

Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems...

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Bibliographic Details
Published in:Future internet Vol. 17; no. 9; p. 380
Main Authors: Panhwar, Kalsoom, Soomro, Bushra Naz, Bhatti, Sania, Jaskani, Fawwad Hassan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2025
Subjects:
Accuracy
Algorithms
Architecture
Artificial intelligence
Artificial neural networks
attention mechanisms
Cloud computing
CNN–LSTM hybrid model
Coders
Computer architecture
Data integration
Deep learning
Desertification
desertification prediction
Design
Early warning systems
Earth observations (from space)
Edge computing
Environmental management
Environmental monitoring
Environmental protection
Environmental sustainability
Forecasting
Image resolution
Infrastructure
Internet
Literature reviews
Machine learning
Monitoring systems
Neural networks
Optimization techniques
Real time
Remote sensing
Satellite imagery
Satellite imaging
Satellite observation
satellite remote sensing
Smart cities
Temporal resolution
Vegetation
China
Pakistan
ISSN:1999-5903, 1999-5903
Online Access:Get full text
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Summary:Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems. This study presents a novel Spatio-Temporal Desertification Predictor (STDP) framework that integrates deep learning with next-generation satellite imaging for time-series desertification forecasting. The proposed encoder–decoder architecture combines Convolutional Neural Networks (CNNs) for spatial feature extraction from high-resolution satellite imagery with modified Long Short-Term Memory (LSTM) networks enhanced by multi-head attention to capture temporal dependencies. Environmental variables are fused through an adaptive data integration layer, and hyperparameter optimization is employed to enhance model performance for edge computing deployment. Experimental validation on a 15-year satellite dataset (2010–2024) demonstrates superior performance with MSE = 0.018, MAE = 0.079, and R2=0.94, outperforming traditional CNN-only, LSTM-only, and hybrid baselines by 15–20% in prediction accuracy. The framework forecasts desertification trends through 2030, providing actionable signals for environmental management and policy-making. This work advances the integration of AI with satellite-based Earth observation, offering a scalable path for real-time environmental monitoring in IoT and edge computing infrastructures.
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi17090380