Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced information at the urban scale. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are fine on geometrical information but are often located in local coordinates and limited at an individual building level. In this study, we propose a complementary strategy to solve the contradiction between fine and large-scale construction of GeoBIM in urban scenes. A deep learning framework and graph theory are combined for LiDAR point cloud segmentation. Then, a coarse-to-fine matching program is developed to integrate building point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of building BIM models are 0.156 m, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.

The Sentinel-2 MSI and Landsat-8 OLI are often used as data sources to form temporally dense harmonization observations for accurate quantitative studies. However, challenges remain in resolving surface reflectance differences between both sensors due to inconsistencies in spectral band positions, widths, and spectral response functions. In this study, we developed the Matching bands Adjustment before Missing bands Prediction method (MA-MP) for performing spectral harmonization between Landsat-8 and Sentinel-2A to reduce such surface reflectance differences. This method uses the global representative samples from the Harmonized Landsat Sentinel-2 (HLS) Product to build the spectral harmonization model of Landsat-8 and Sentinel-2A at the cluster level for bandpass adjustment in the bands where Landsat-8 and Sentinel-2A are matched, and for prediction in the band which is unilaterally missing bands in Landsat-8 image relative to Sentinel-2A image. Experimental results show robust spectral harmonization in four selected study areas with a variety of land cover types, and the spectral harmonization results are significantly better than those of current mainstream methods. Overall, the proposed MA-MP can adjust the observations of Landsat-8 and Sentinel-2A in matching bands and predict the observations of Landsat-8 in a unilaterally missing band, which has the potential to be used flexibly for building the spectral harmonization model between any two different sensor images.

Efficiently and accurately measuring forest structure is of great significance for high-quality assessment of forest resources. Backpack laser scanning (BLS) has become a common device to acquire forest structural information due to its low cost and high time efficiency. However, complex forest environments bring challenges to BLS-based forest mapping, which faces problems with incomplete data and poor mapping accuracy. In this article, we design a disassembly-free dual-scanner BLS system for complete and accurate forest mapping. We first execute a high precision automatic self-calibration of dual laser scanners by means of angle compensation and the fixed rotation angle. Then, a simultaneous localization and mapping (SLAM) framework by combining the natural feature of trees and Inertial Measurement Unit (IMU) measurements is proposed, in which IMU provides priori motion estimation and motion compensation for dual scanners, and the natural feature of the forest is used to correct motion. The proposed method is validated in three small-scale forest plots with size of 0.1 ha. Experimental results show well performance in terms of mapping accuracy, where the mean errors and the root square mean errors are less than 3.0 cm in both horizontal and vertical directions. Our study demonstrates the effectiveness of the proposed strategy and has the potential to perform accurate and complete mapping in understory.

Fine-scale 3-D morphological features of rocks on the Martian surface provide important clues to Mars exploration missions and scientific discoveries. To obtain such information, an automatic approach for high-precision measurement of 3-D morphological features of Martian rocks is proposed in this letter. The approach directly detects 3-D rocks from dense point cloud that is generated by interpolating triangulated irregular network (TIN) model, through a coarse-to-fine process combining cloth simulation filtering (CSF) and connected-component labeling algorithms. Multiple 3-D morphological features are then precisely extracted by modeling rock points and fitting local ground plane. Experimental results demonstrate that the proposed approach provides an effective way for the extraction of complete 3-D rocks and comprehensive morphological features with over 90% accuracy.

Synthetic aperture radar (SAR) can be used to obtain remote sensing images of different growth stages of crops under all weather conditions. Such time-series SAR images can provide an abundance of temporal and spatial features for use in large-scale crop mapping and analysis. In this study, we propose a temporal feature-based segmentation (TFBS) model for accurate crop mapping using time-series SAR images. This model first extracts deep-seated temporal features and then learns the spatial context of the extracted temporal features for crop mapping. The results indicate that the TFBS model significantly outperforms traditional long short-term memory (LSTM), U-network, and convolutional LSTM models in crop mapping based on time-series SAR images. TFBS demonstrates better generalizability than other models in the study area, which makes it more transferable, and the results show that data augmentation can significantly improve this generalizability. The visualization of the temporal features extracted by the TFBS shows that there is a high degree of intraclass homogeneity among rice fields and interclass heterogeneity between rice fields and other features. TFBS also achieved the highest accuracy of the four deep learning models for multicrop classification in the study area. This study presents a feasible way of producing high-accuracy large-scale crop maps based on the proposed model.

The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm.