In an urban area, the roof is the only available surface that can be utilized for installing solar photovoltaics (PV), and the active surface area depends on the type of roof. Shadows on a solar panel can be caused by nearby tall buildings, construction materials such as water tanks, or the roof configuration itself. The azimuth angle of the sun varies, based on the season and the time of day. Therefore, the simulation of shadow for one or two days or using the rule of thumb may not be sufficient to evaluate shadow effects on solar panels throughout the year. In this paper, a methodology for estimating the solar potential of solar PV on rooftops is presented, which is particularly applicable to urban areas. The objective of this method is to assess how roof type and shadow play a role in potentiality and financial benefit. The method starts with roof type extraction from high-resolution satellite imagery, using Object Base Image Analysis (OBIA), the generation of a 3D structure from height data and roof type, the simulation of shadow throughout the year, and the identification of potential and financial prospects. Based on the results obtained, the system seems to be adequate for calculating the financial benefits of solar PV to a very fine scale. The payback period varied from 7–13 years depending on the roof type, direction, and shadow impact. Based on the potentiality, a homeowner can make a profit of up to 200%. This method could help homeowners to identify potential roof area and economic interest.

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.

Accurate and timely information regarding the locations and types of crops cultivated is essential for sustainable agriculture and ensuring food security. However, accurately mapping season-specific crop types in tropical and subtropical regions is challenging due to smallholder farms, fragmented fields, predominant clouds, and limited seasonal reference data. To address these limitations, this study employed optical and radar satellite data in conjunction with machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Gradient Tree Boosting (GBoost), utilizing a large number of reference datasets across crop seasons. To validate the results, extensive field visits were undertaken throughout the year. Our focus centered on two regions in Thailand recognized for their small fields and frequent overcast conditions. Utilizing over 8000 reference points, we mapped 12 crop types in Chaiyaphum province and 13 crop types in Suphan Buri province for three cropping seasons in 2023. The RF algorithm proved to be the most effective, demonstrating superior performance across all seasons in comparison to the other models, achieving an overall accuracy exceeding 85%, with classifications for sugarcane and rice exceeding 90%. The resultant maps identified sugarcane, rice, and cassava as the principal crops in the region. This research exemplifies a methodology for producing highly accurate seasonal crop maps, providing valuable tools for making informed decisions for crop sustainable management, thereby supporting sustainable agriculture practices. Our findings underscore the potential of Earth observation satellites and machine learning algorithms in addressing significant agricultural challenges and facilitating the development of more resilient strategies for food security.