Xi Cheng

Fine-grained classification of urban green spaces (UGSs) is important for urban ecological assessment and management but remains challenging because of spectral similarity among vegetation types and inaccurate object delineation in complex urban environments. This study proposes a pixel-to-object framework that combines semantic segmentation-guided object construction with multi-periodic phenological modeling. A semantic green-space mask derived from 0.27 m very-high-resolution imagery constrains superpixel segmentation to generate spatially coherent, boundary-aware green space object-level patches (GSOPs). Pixel-level temporal representations are then derived from Sentinel-2 normalized difference vegetation index (NDVI) time series using TimesNet, aggregated into GSOP-level phenological features, and combined with spatial attributes to classify urban trees, grasslands, and farmlands. Applied to the built-up area of Chengdu, China, the framework achieved an overall accuracy of 91.6%, with F1-scores of 92.5%, 91.9%, and 87.6% for urban trees, grasslands, and farmlands, respectively. Ablation experiments showed that removing phenological features reduced overall accuracy by 13.1 percentage points and decreased the F1-scores of grasslands and farmlands by 16.0 and 23.0 percentage points, respectively. These results demonstrate that semantically constrained object delineation and phenological information jointly reduce boundary fragmentation and improve the discrimination of spectrally similar urban vegetation types.

Urban vacant land (UVL) presents significant environmental and urban planning challenges as cities expand, necessitating effective identification and management strategies. This study proposes an enhanced framework for UVL extraction, based on an improved Segformer model, which incorporates the densely connected atrous spatial pyramid pooling module and the progressive feature pyramid network for expanded receptive field and achieve multi-scale feature integration. The framework first applies a region-based stratification approach, dividing the study area into the central and expanded Areas to handle varying land characteristics in different urban regions. Both pre-trained and non-pre-trained models were utilized to assess their effectiveness in segmentation accuracy, using high-resolution remote sensing images of Chengdu. Experimental results demonstrate the effectiveness of the framework, with the pre-trained model, trained on urbanized area data from Chinese cities, achieving F1-Scores of 91.34 and 90.05 and IoU values of 84.21 and 81.91 for the Central and Expanded Areas, respectively. In contrast, the non-pre-trained model yielded F1-Scores of 93.08 and 92.32, with corresponding IoU values of 87.16 and 85.74. Ablation studies and robustness tests further confirm the model's stability and precision in complex application scenarios. This framework provides an accurate and efficient tool for UVL identification, contributing to improved urban land utilization and offering valuable insights for future research and urban planning.

Addressing the challenges related to urban water bodies is essential for advancing urban planning and development. Therefore, obtaining precise and timely information regarding urban water bodies is of paramount importance. To address issues such as incomplete extraction boundaries, mistaken feature identification, and omission of small water bodies, this study utilized very high-resolution (VHR) satellite images of the Chengdu urban area and its surroundings to create the Chengdu Urban Water Bodies Semantic Segmentation Dataset (CDUWD). Based on the shape characteristics of water bodies, these images were processed through annotation, cropping, and other operations. We introduced Ad-SegFormer, an enhanced model based on SegFormer, which integrates a densely connected atrous spatial pyramid pooling module (DenseASPP) and progressive feature pyramid network (AFPN) to better handle the multi-scale characteristics of urban water bodies. The experimental results demonstrate the effectiveness of combining the CDUWD dataset with the Ad-SegFormer model for large-scale urban water body extraction, achieving accuracy rates exceeding 96%. This study demonstrates the effectiveness of Ad-SegFormer in improving water body extraction and provides a valuable reference for extracting large-scale urban water body information using VHR images.

Change detection (CD) is the main task in the remote sensing field. Binary change detection (BCD), which only focuses on the region of change, cannot meet current needs. Semantic change detection (SCD) is pivotal for identifying regions of change in sequential remote sensing imagery, focusing on discerning “from-to” transitions in land cover. The emphasis on features within these regions of change is critical for SCD efficacy. Traditional methodologies, however, often overlook this aspect. In order to address this gap, we introduce a change-aware guided multi-task network (CGMNet). This innovative network integrates a change-aware mask branch, leveraging prior knowledge of regions of change to enhance land cover classification in dual temporal remote sensing images. This strategic focus allows for the more accurate identification of altered regions. Furthermore, to navigate the complexities of remote sensing environments, we develop a global and local attention mechanism (GLAM). This mechanism adeptly captures both overarching and fine-grained spatial details, facilitating more nuanced analysis. Our rigorous testing on two public datasets using state-of-the-art methods yielded impressive results. CGMNet achieved Overall Score metrics of 58.77% on the Landsat-SCD dataset and 37.06% on the SECOND dataset. These outcomes not only demonstrate the exceptional performance of the method but also signify its superiority over other comparative algorithms.

Urban water runs through and plays an important role in urban development, and understanding the spatial changes of urban water bodies is significant. However, the rapid growth of remote sensing data caused by rapid urbanization poses a challenge to the efficiency of water extraction methods. In this paper, a high-precision method with a lightweight structure for urban water extraction was proposed in terms of the dilemma between efficiency and accuracy. We designed SE-ARM, SE-FFM, and DW-ASPP on the basis of BiSeNet to improve it in the water semantic segmentation application, which resulted in SE-BiSeNet. To validate the proposed model’s effectiveness, we compared our SE-BiSeNet to other advanced water extraction methods on the Chengdu dataset in terms of accuracy and efficiency. The results showed that the proposed model performs well in both accuracy (0.96 IoU) and efficiency (6.4 FPS on a GTX 1060 card) with fewer parameters and calculations, which indicates an objective application potential.