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The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes.

In simultaneous localization and mapping (SLAM), loop closure detection is a significant yet still open problem. It contributes to construct a globally consistent and accurate map. This paper proposes a fast and compact loop closure detection method (FastLCD) based on comprehensive descriptors and machine learning to achieve reliable and precise results using 3D point cloud for indoor LiDAR mobile mapping. Comprehensive descriptors proposed in this paper encode discriminative multimodality features to describe each scan of point clouds. The specific values of descriptors of point cloud scan pairs are fed into a machine learning model. We leverage the pre-trained learning model as a classifier to distinguish whether a pair of laser scans is a loop candidate. Then, to ensure the results’ precision, a novel double-deck loop candidate verification strategy is used to reject false positives. The algorithm is evaluated on datasets of some typical indoor environments. Compared with some state-of-the-art loop closure detection algorithms, the proposed FastLCD algorithm demonstrates superior performance in precision and recall rate. Moreover, the method proposed also exhibits high time efficiency, excellent generalization performance and insensitivity to threshold changes.