Accurately assessing rooftop photovoltaic (PV) potential requires precise identification of rooftop areas and availability. Current deep learning approaches using aerial imagery are faced with two challenges: inconsistent rooftop appearances caused by varying solar azimuths tend to mislead rooftop orientation extraction, and the existence of ancillary rooftop facilities often results in overestimation of solar potential. To tackle these challenges, a novel framework is proposed, with three components: automated extraction of rooftop areas and orientations, appearance-based estimation of rooftop availability coefficients, and PV potential calculation via a multi-orientation quantitative integration strategy. The segmentation network identifies geometric boundaries of rooftops and categorizes pitched roof segments into orientation-specific categories. High-level features of rooftop segments are then extracted from deep networks and clustered to compute availability coefficients at segment-level. Finally, the integration strategy leverages the symmetry assumption of sloped rooftops to mitigate classification errors and improve robustness in solar potential computation. Our framework is trained on the RID dataset with different category definition schemes, and estimation results are compared with solar radiation flux provided by NASA POWER. The overall relative error is less than 1%, which demonstrates the effectiveness of our framework.

Interferometric Synthetic Aperture Radar (InSAR)-derived Digital Elevation Models (DEMs) provide critical landform data for monitoring the stability of urban infrastructure, especially for linear infrastructure such as roads and transportation corridors. Traditional landform classification methods are often hindered by incomplete results and require significant manual intervention. To address these challenges, we propose an automated landform classification method based on an enhanced Random Forest (RF) model that integrates Optimization of Decreasing Reduction (ODR) for majority class undersampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVM-SMOTE) for minority class oversampling, specifically to address class imbalance. The method was validated using a dataset of 82,450 expert-labeled samples from approximately 100 km of highway corridors, with independent test sets and ten-fold cross-validation. The enhanced RF model achieved a classification completeness rate of 100% and a macro F-score of 97.0%, significantly outperforming traditional rule-based and standard RF methods. This approach provides robust post-processing support for InSAR-based urban infrastructure monitoring and environmental modeling.