Advancements in Earth observation technology enable the detection of surface changes in intricate urban environments. Building change detection (BCD) plays a crucial role in urban planning and environmental monitoring. However, existing deep learning-based BCD algorithms exhibit limited capability in feature extraction, feature relationship comprehension, sample imbalance mitigation, and accurate boundary identification for changed objects. To address these challenges, we introduce an attention-guided edge refinement network (AERNet) that employs a global context feature aggregation module (GCFAM) to aggregate information from extracted multi-layer context features. Our approach incorporates an attention decoding block (ADB) guided by enhanced coordinate attention (ECA) to capture channel and location associations between features. Furthermore, we utilize an edge refinement module (ERM) to enhance the network’s capacity to sense and refine the edges of changed areas. To tackle the issue of class imbalance and augment the algorithm’s feature learning ability, we devise a novel self-adaptive weighted binary cross-entropy (SWBCE) loss function, combined with a deep supervision (DS) strategy. Experiments are conducted on two publicly available datasets, GDSCD and LEVIR-CD, as well as our newly developed high-resolution complex urban scene BCD dataset, i.e., HRCUS-CD. The latter dataset comprises 11,388 pairs of images at 0.5-meter resolution and over 12,000 labeled change buildings. Comparative experiments indicate that AERNet surpasses advanced competitive methods, while ablation experiments demonstrate the effectiveness of AERNet’s model components and the SWBCE loss function. Efficiency comparison confirms that AERNet achieves comprehensive detection performance with superior effectiveness and robustness.

Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

The traditional method of obtaining rock quality designation (RQD) cannot fully reflect the anisotropy of the rock mass and thus cannot accurately reflect its quality. In the method of calculating RQD based on three-dimensional network simulation of discontinuities, due to the limited number of samples and low accuracy of discontinuity data obtained by manual contact measurement, a certain deviation in the network is generated based on the data, which has an impact on the calculation result. Taking a typical slope in Dongsheng quarry in Changchun City as an example, in this study, we obtained the discontinuity data of the slope based on digital close-range photogrammetry, which greatly enlarged the sample size of discontinuity data and improved the data quality. Based on the heterogeneity of the rock mass, the optimum threshold of discontinuity spacing was determined when surveying lines were laid parallel to different coordinate axes to calculate the generalized RQD, and the influence of measuring blank areas on the slope caused by vegetation coverage or gravel accumulation was eliminated. The real generalized RQD of the rock mass after eliminating the influence of blank areas was obtained. Experiments showed that, after eliminating the influence of blank areas, the generalized RQD of the slope rock mass more truly represented the complete quality of rock mass and offers a new idea for the quality evaluation of engineering rock mass.

Due to earthquakes and large-scale exploitation of oil, gas, groundwater, and coal energy, large-scope surface deformation has occurred in Songyuan City, Jilin Province, China, and it is posing a serious threat to sustainable development, including urban development, energy utilization, environmental protection, and construction to improve saline–alkali land. In this study, we selected the Chagan Lake region, which is located in Songyuan City, as our research area. Using temporarily coherent point synthetic aperture radar interferometry (TCPInSAR), we obtained a time series of land surface deformation and the deformation rate in this area from 20 ALOS PALSAR images from 2006 to 2010. The results showed that the deformation rate in the Chagan Lake region ranged from −46.7 mm/year to 41.7 mm/year during the monitoring period. In three typical land cover areas of the Chagan Lake region, the subsidence in the wetland area was larger than that in the saline–alkali area, while the highway experienced a small uplift. In addition, surface deformation in lakeside areas with or without dykes was different; however, as this was mainly affected by soil freeze–thaw cycles and changes in groundwater level, the deformation showed a negative correlation with temperature and precipitation. By monitoring and analyzing surface deformation, we can provide a data reference and scientific basis for sustainable ecological and economic development in the Chagan Lake region and adjacent areas.