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

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Argitaratua izan da:Future Internet vol. 17, no. 9 (2025), p. 380-412
Egile nagusia: Panhwar Kalsoom
Beste egile batzuk: Soomro Bushra Naz, Bhatti Sania, Jaskani Fawwad Hassan
Argitaratua:
MDPI AG
Gaiak:
Pakistan
China
Accuracy
Temporal resolution
Desertification
Internet
Deep learning
Image resolution
Computer architecture
Early warning systems
Environmental monitoring
Optimization techniques
Artificial neural networks
Satellite imagery
Edge computing
Architecture
Data integration
Machine learning
Monitoring systems
Coders
Smart cities
Environmental management
Vegetation
Remote sensing
Earth observations (from space)
Artificial intelligence
Infrastructure
Design
Algorithms
Literature reviews
Satellite observation
Real time
Cloud computing
Forecasting
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Laburpena: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 <inline-formula>R2=0.94</inline-formula>, 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.
ISSN:1999-5903
DOI:10.3390/fi17090380
Baliabidea:ABI/INFORM Global