Building height is a key metric in transitioning urban analysis from two-dimensional to three-dimensional perspectives. It serves as a fundamental indicator for assessing urban development, population density, and energy consumption. Regardless of whether machine learning or deep learning methods are used, accurate height estimation inevitably relies on large quantities of labeled building height samples for model training. However, existing reference datasets often suffer from ambiguity due to floor-to-height conversions and temporal obsolescence. The advent of spaceborne light detection and ranging (LiDAR) provides a promising avenue to overcome these limitations, enabling the acquisition of large-scale, high-precision, and time-sensitive reference height data. Moreover, while buildings are inherently individual and discrete units, most existing height estimation studies operate at gridded scales, thereby obscuring inter-building height variability. In this study, we propose a novel approach to estimate individual building heights by integrating spaceborne LiDAR with multisource remote sensing data. First, leveraging building footprint data, we derive reference height samples by jointly retrieving building heights from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and the Global Ecosystem Dynamics Investigation (GEDI). Then, we construct a multimodal feature set by extracting seasonal features from a combination of multisource datasets, such as Sentinel-1, Sentinel-2, and SDGSAT-1. To model the spatially complex height distribution, we propose the Neighborhood-aware CNN-Transformer Network (NeCT-Net), which estimates building heights at the pixel level. To address the scarcity of tall building samples, we incorporate a semi-supervised learning paradigm. Specifically, pseudo-labels for high-rise buildings are generated using predictions from a teacher model and iteratively refined through self-training, thereby enhancing the student model’s learning capacity in tall-structure scenarios. Finally, we propose a post-processing approach that constrains height predictions using building morphological information, enabling the conversion from pixel-level predictions to vector-based building-level estimations. We apply the proposed method to two iconic urban areas—Beijing and Seattle. The results demonstrate strong predictive performance, with RMSE, MAE, and R values of 9.01 m, 6.08 m, and 0.93 for Beijing, and 8.72 m, 5.31 m, and 0.79 for Seattle. Compared to baseline methods, our approach reduces RMSE by 29.66% in Beijing and 7.43% in Seattle, while improving R by 47.62% and 46.06%. When benchmarked against existing individual building height products, our model achieves RMSE reductions of 31.43% and 21.16%, and R improvements of 57.63% and 23.44% in the two cities. These results highlight the methodological advancement and robustness of our approach. This work presents a new paradigm for large-scale, individual building height estimation through the integration of spaceborne LiDAR and multisource remote sensing data. The source code will be made available at https://github.com/Lunar-Elf/NeCT-Net.

For estimating ground object height from single remote sensing images, this study proposes a land use knowledge guided multiscale height estimation network (LUMNet), which takes single image and land use data as inputs and produces an estimated height map as output. First, in the encoder part, the visual geometry group network (VGGNet) is used to extract multilevel deep semantic features from images. Second, the features of the encoder part are fused with those of the decoder part through skip connection with land use knowledge attention weight and Dense Atrous Spatial Pyramid Pooling (DenseASPP). Third, in the decoder part, the features are decoded using upsampling and convolution, and a joint loss function is constructed to supervise network training. Experiments show that the proposed method achieves the best visualizations and quantitative evaluation results among all the tested methods. For the Vaihingen dataset, theRMSE,MAE, andZNCCof LUMNet are 1.523, 0.969, and 0.908, respectively. For the Potsdam dataset, these are 2.158, 1.222, and 0.871, respectively. The source code of LUMNet has been made public at the following link: https://figshare.com/s/eaf206879ade35a61f88.

Automatic open-pit mine extraction and change detection from high-resolution remote sensing images are of great importance to mineral resource management. However, the high spatial heterogeneity and spectral variations of mining area scenarios make these tasks challenging. Motivated by the strong correlation between the two tasks and their potential mutual benefits, this article presents a hybrid convolutional neural network (CNN)–Transformer multitask network (CTMNet). Constructed in an encoder-decoder manner, CTMNet has two sperate extraction paths (EPs) to localize the regions of interest for bi-temporal images, along with a change detection path (CDP) to identify discrepancies by differentiating the multiscale feature representations from the EPs. As the basic building block for the EP, a CNN-Transformer hybrid block is designed to enhance the global and local feature representation capacity. To cope with the variations in the bi-temporal images, we propose the feature alignment module for the CDP. A hard sample mining-based contrastive constraint loss is proposed to emphasize the contributions of hard samples to the training process. The experimental results on a collected open-pit mine extraction and change detection dataset (OMECSet) and two public datasets reveal the validity of the CTMNet when compared to the state-of-the-art methods. The OMECSet and the code of CTMNet have been made public available athttps://figshare.com/s/80519cb980ca54456447.

Automatic change detection of open-pit mines from high-resolution remote sensing images is of great significance for the mining and management of mineral resources. For this purpose, we propose a siamese multiscale change detection network (SMCDNet) with an encoder-decoder structure. First, the multiscale low-level and high-level features of the bi-temporal image are extracted by a siamese network. Second, a multilevel feature absolute difference (MFAD) module is proposed to fuse the low-level and high-level change features. Finally, convolution and up-sampling operations are used to recover the details of the changed areas. A self-made open-pit mine change detection (OMCD) dataset is employed to conduct experiments. Experimental results have demonstrated that the proposed method is superior to the comparison networks.$F1$- score of 88.13% is achieved by the proposed SMCDNet. The OMCD dataset produced in this study has been made public at the following link:https://figshare.com/s/ae4e8c808b67543d41e9.