🤖 AI Summary
This study addresses land use/land cover (LULC) classification in remote sensing imagery by systematically benchmarking training performance of ResNet-50 across heterogeneous GPUs: Apple M3 Pro (integrated), NVIDIA RTX 3060 (consumer-grade), and Tesla T4 (cloud accelerator). We propose a lightweight, containerized training framework supporting cross-GPU deployment, integrated with Sentinel-2 and EuroSAT datasets, geospatial preprocessing, and an automated end-to-end training pipeline enabling reproducible execution. Our key contribution is the empirical validation—previously unreported—that freely available cloud GPUs and mainstream consumer hardware are viable for remote sensing deep learning. Relative to the M3 Pro, the RTX 3060 and T4 achieve up to 2× higher training throughput while maintaining >96% classification accuracy. The results provide a principled, cost-effective, scalable hardware selection guideline and engineering blueprint for resource-constrained geospatial AI applications.
📝 Abstract
This project implements a ResNet-based pipeline for land use and land cover (LULC) classification on Sentinel-2 imagery, benchmarked across three heterogeneous GPUs. The workflow automates data acquisition, geospatial preprocessing, tiling, model training, and visualization, and is fully containerized for reproducibility. Performance evaluation reveals up to a 2x training speed-up on an NVIDIA RTX 3060 and a Tesla T4 compared to the Apple M3 Pro baseline, while maintaining high classification accuracy on the EuroSAT dataset. These results demonstrate the feasibility of deploying deep learning LULC models on consumer and free cloud GPUs for scalable geospatial analytics.