<!-- *** Custom HTML *** --> In recent years, ecological and environmental degradations have occurred widely around the world, especially in urban areas with rapid economic development, intensive human activities, and massive urbanization. We aim at proposing an effective and comprehensive urban ecological environment evaluation method based on remote sensing. Harmonic analysis of time series method and spatial–temporal information fusion based on nonlocal means filter was used to improve the quality of long-term remote sensing data from 2003 to 2019. The greenness, dryness, heat, and atmospheric turbidity of the ecological environment were represented by normalized difference vegetation index, normalized difference built-up index, land surface temperature, and aerosol optical depth, respectively. An urban ecological comfort index (UECI) was established by the weighted combination of the above four indicators using entropy method. UECI was calculated at seasonal and annual scales from 2003 to 2019 in Hefei city, China, as a case study. Furthermore, Mann–Kendall trend test, spatial autocorrelation analysis, and spatial clustering analysis were performed on the UECI results. The experimental results show that UECI in Hefei had an increasing trend from 0.5446 to 0.5737 between 2003 and 2019, indicating that its ecological environment is actively improving. UECI is roughly equal in spring, summer, and autumn, and the worst is in winter. The responses of UECI to urban expansion, environmental protection policies, and extreme weather were also explored. Its sensitively analysis results and the fact that the spatial–temporal trends of UECI were consistent with the influencing factors reflected the stability and reliability of UECI, indicating that the proposed UECI performed well in Hefei and could be applied to measure urban ecological comfort level in other study areas.

Recently, self-supervised pretraining algorithms have been extensively investigated in the field of remote sensing, yielding significant performance improvements in change detection tasks. However, many existing algorithms inadequately exploit multi-level features, which impedes their ability to precisely extract salient change characteristics and accurately determine the structural integrity of changed targets. Moreover, most mainstream approaches tend to neglect the mining of shallow, frequency-domain details, thereby limiting the model's capacity to discern object boundaries. To address these challenges, we propose a novel multi-dimensional feature alignment self-supervised pretraining framework, termed MDFANet. Specifically, we introduce a Hierarchical Decoupling and Multi-dimensional Feature Alignment (HDMFA) mechanism that facilitates the precise capture of multiple change cues at different levels through decoupled modeling of deep and shallow features and the design of multi-dimensional alignment branches. Additionally, we develop a Shallow Frequency-domain Feature Alignment (SFFA) branch that emphasizes the high-frequency components in the input data, effectively compensating for the inadequate representation of edge features in the spatial domain and enhancing the model's ability to detect complex boundaries. Experimental results on LEVIR-CD, SYSU-CD and EGY-BCD datasets validate the superiority of MDFANet, which outperforms SSLCD, Cmid and other SOTA methods by 1.7–2.3% in F1 score.

Recently, significant progress has been made in remote sensing image semantic segmentation based on attention and frequency. However, current attention mechanisms disregard the difference information between similar features, which reduces interclass variance and increases the difficulty of class discrimination. Meanwhile, the frequency domain’s limited representation of multidirectional edges and textures, coupled with weak feature complementarity in multiscale architectures, respectively, hinders contour capture and spatial localization of small objects. To address these issues, we propose a difference-guided multidirectional complementary network (DGMDC-Net). Specifically, a difference attention-guided aggregation module is constructed, which amplifies the differences between similar features in the aggregated information and serves as a guidance signal to reconstruct the response distribution of the global attention map, thereby facilitating category discrimination ability. Furthermore, we design a multidirectional frequency enhancement module, which amplifies the multidirectional characteristics of pixel intensity mutations in wavelet domain while supplementing the structural patterns of intensity distribution within local regions to refine the contour of small objects. In addition, a multilevel complementary module is developed, which correct detail deviations in deep layers while maintaining shallow contextual scene perception, to more accurately localize small targets. Experimental results on four benchmark datasets demonstrate that DGMDC-Net achieves superior performance in remote sensing image segmentation tasks.

The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can be directly available for multi-level tile images in a pixel representation form. The proposed cross-layer raster tile map rendering method (CRTMRM) consists of four primary aspects: geographic object separation, consistency rendering rules, data scaling and derivation with super-pixel segmentation, and inconsistency detection. The scale transformation strategy with the super-pixel attempts to obtain a simplified representation. Taking the scale lifespan variation and geometric consistency rules into account, the inconsistency detection of tile maps is conducted between temporal versions, multi-sources, and different scales through actual and derived data overlay analysis. The experiment focuses on features of cross-layer water or vegetation areas with Level 9 to Level 14 in Baidu Maps, Amap, and Google Maps. This method is able to serve as a basis for massive unstructured web map data inconsistency detection and support intelligent web map rendering.

1. Cartographic generalisation is a fundamental process for enabling multi-scale geographic feature representation, with the aim of transforming detailed geographic information from a source scale ...

Air temperature is a fundamental indicator for climate monitoring, agricultural planning, and ecosystem management. Land Surface Temperature (LST) retrieved from thermal remote sensing is widely used as a critical proxy for air temperature. However, existing air temperature estimation studies predominantly rely on single-date LST and diurnal variations (e.g., the Diurnal Temperature Cycle, DTC), overlooking the potential of multi-day LST temporal dependencies and lacking explicit spatial representativeness assessments. To address these challenges, here we present a hybrid Transformer-CNN daily mean air temperature (Ta) estimation network (HyTC-TaNet) which integrates Transformer-based temporal attention with convolutional feature extraction. While the Area of Applicability (AOA) metric is introduced to quantify spatial prediction confidence. Comparative experiments involving eight models, including HyTC-TaNet and seven benchmark algorithms, reveal that a 6-7 days temporal input window yields the best performance across all models. HyTC-TaNet achieves the highest accuracy, with RMSE = 1.429 dddddddddddddddddddddddddddddC, MAE = 1.101 ddddddddddddddddddddddddddddddddddC, and R222222222222222222222222 = 0.976, reducing errors by 0.273 dddddddddddddddddddddddC (RMSE) and 0.219 dddddddddddddddddddddddddddddddddddddddC (MAE) compared with concurrent day input. Spatial-temporal analysis further confirms that the optimal temporal sequence enriches historical and trend information, significantly enhancing estimation stability and spatial detail. Furthermore, the AOA of the model was assessed, confirming robust applicability in station-dense plains while effectively flagging extrapolation risks over large water bodies and high-altitude ridges due to limited representativeness in the training data. These findings demonstrate the strong potential of coupling HyTC-TaNet with optimally selected multi-day time series and spatial applicability assessment for precise, scalable air temperature estimation.