AI Enabled Geospatial Intelligence for the Energy Transition: A Comparative CNN Study on CCUS, Geothermal Siting, and NetZero Strategies

Practical guidance about tradeoff choices between accuracy, efficiency and deployment ability in deep convolutional neural network architectures for land use and land cover classification has been largely unavailable because each study evaluates architectures differently. This paper provides a controlled comparative assessment of four popular convolutional neural network architectures for land use and land cover classification visual geometry group19, ResNet50, Inception_Version3 and MobileNet_V2 using the Euro_SAT benchmark which includes 27,000 Sentinel2 red green blue images that have been cut into 10 land uses classes. The convolutional neural network architectures were all trained and evaluated through the same preprocessing, augmentation, data splitting, training procedure and metric as follows: Overall accuracy/F1 macro averaging class by class confusion matrix convergence dynamics efficiency metrics (number of parameters and inference-oriented considerations). The results indicate that modern architectures provide significantly better than older sequential baselines: ResNet50 provided the highest total accuracy (>97%), along with consistent convergence behavior; InceptionV3 improved discrimination for classes with both ambiguous visual appearances and linear structures (river, highway); MobileNetV2 was able to achieve high accuracy (>94%), but had an order of magnitude less number of parameters than the other architectures and is well suited to lower source or real time application scenarios. Finally, this paper maps convolutional neural network outputs into an energy transition decision workflow (Renewable Siting → Corridor Constraints → Monitoring), and demonstrates how land use and land cover layers derived from convolutional neural networks can support net zero resource planning. • Benchmarks 4 CNNs on EuroSAT using identical training and evaluation setup. • ResNet50 achieved highest accuracy (>97%) for LULC classification. • MobileNetV2 reduced parameters by ∼6× while maintaining >94% accuracy. • InceptionV3 improved corridor feature detection (roads, rivers). • Enables decision-grade LULC for CCUS, geothermal, and net-zero planning.