Multimodal Artificial Intelligence for Integrating Remote Sensing, IoT, and Environmental Data for Sustainable Resource Intelligence

Abstract

The accelerating global demand for sustainable management of natural resources including fresh water, arable land, forests, and atmospheric systems necessitates intelligent, real-time, and spatially comprehensive monitoring frameworks that no single observational modality can provide. Remote sensing satellites deliver synoptic spatial coverage but lack ground-level temporal granularity; IoT sensor networks provide high-frequency localized measurements but cannot scale to regional extents; and environmental databases supply historical context but remain disconnected from real-time observational streams. This paper proposes MultiModal-SRI (Multimodal Sustainable Resource Intelligence) a unified multimodal artificial intelligence framework that fuses remote sensing imagery, heterogeneous IoT sensor streams, and curated environmental databases through a three-branch deep learning architecture to deliver spatially continuous, temporally dense, and contextually enriched sustainable resource intelligence. The framework employs a Convolutional Neural Network branch for spatial feature extraction from Sentinel-2 and Landsat-8 imagery, a Bidirectional Long Short-Term Memory branch for temporal pattern modeling of IoT time-series streams, and a Transformer-based cross-modal attention branch for dynamic inter-modality feature alignment and fusion. The fused multimodal representation is decoded into five resource intelligence targets: Water Availability Index, Soil Moisture Status, Vegetation Stress Index, Air Quality Level, and Urban Resource Efficiency Score. MultiModal-SRI is evaluated across four geographically and ecologically diverse study regions an agricultural river basin, an industrial coastal zone, a semi-arid dryland region, and a mixed urban-rural transitional area using a dataset comprising 1.2 million IoT sensor readings, 3,840 satellite image scenes, and 14 years of historical environmental records from 2009 to 2023.