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

Kalsoom Panhwar (), Bushra Naz Soomro (), Sania Bhatti and Fawwad Hassan Jaskani
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Kalsoom Panhwar: Department of Computer Systems Engineering, University of Sindh, Jamshoro 76080, Pakistan
Bushra Naz Soomro: Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan
Sania Bhatti: Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan
Fawwad Hassan Jaskani: Department of Computer Systems Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Future Internet, 2025, vol. 17, issue 9, 1-32

Abstract: 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 R 2 = 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.

Keywords: deep learning; satellite remote sensing; desertification prediction; attention mechanisms; CNN–LSTM hybrid model; environmental monitoring; edge computing; IoT applications; time-series forecasting; spatio-temporal modeling (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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