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The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method's applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields. [ABSTRACT FROM AUTHOR]

Different land parcels possess unique microclimates, soils, and biological conditions, which in turn significantly influence the land parcels themselves, impacting biodiversity, hydrological relationships, land degradation, geological disasters, and other ecological environments. Therefore, researching an efficient and accurate method capable of extracting land parcels with the least internal heterogeneity at the macro, meso, and micro scales is extremely important. Multi-scale segmentation, based on scale and resolution analysis techniques, is a bottom-up merging technology that minimizes internal heterogeneity within regions and maximizes heterogeneity between different units. This approach is extensively applied in multi-scale spectral feature extraction and classification and is further combined with deep learning techniques to enhance the accuracy of image classification. This study, using Xinghai County in Qinghai Province as an example, employs multi-scale segmentation and hydrological analysis methods to extract land parcels at different spatial scales. The results show (1) that the land parcels extracted using the hydrological analysis method are catchment units centered around rivers, including slopes on both sides of the river. In contrast, multi-scale segmentation extracts regions comprising land parcels with similar properties, enabling the segregation of slopes and channels into independent units. (2) At a classification threshold of 19, multi-scale segmentation divides the study area into five different types of land parcels, reflecting the heterogeneity of terrain undulations and their hydrological connections. When the classification threshold is set to 31, the study area is divided into 15 types of land parcels, primarily highlighting micro-topographic features. (3) Multi-scale segmentation can merge and categorize areas with the least heterogeneity in land parcels, facilitating subsequent statistical analysis. Therefore, mesoscale land parcels extracted through multi-scale segmentation are invaluable for analyzing regional Earth surface processes such as soil erosion, sediment distribution and transportation. Microscale land parcels are significantly important for identifying high-risk areas in relation to geological disasters like landslides and collapses. [ABSTRACT FROM AUTHOR]