Cultural heritage sites are rare and irreplaceable wealth of human civilization. The majority of them are becoming unstable due to a combination of human and natural disturbances. High-precision, efficient deformation monitoring facilitates the early recognition of potential risks and enables preventive diagnosis of heritage sites. In this study, an advanced Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR) approach was developed by jointly analyzing Persistent Scatterers (PSs) and Distributed Scatterers (DSs) using high-resolution SAR images. Taking the World Heritage Site of Summer Palace in Beijing as the experimental site, deformation resulting from PSs/DSs showed that overall the site was generally stable except for specific areas and/or monuments. Urbanization (construction and demolition) triggered new subsidence in the vicinity of East and South Gate of the site. Slight to moderate (mm/cm-level) instabilities of ruins and monuments on Longevity Hill were detected, perhaps due to a combination of destructive anthropogenic activities and long-term natural decay. Subsidence was also detected along the Kunming Lakeside and was probably attributable to variation of the groundwater level, excessive visitor numbers as well as lack of maintenance. This study presents the potential of the MTInSAR approach for the monitoring and conservation of cultural heritage sites.

One of the major issues in buried archeological sites especially if characterized by intense human activity, complex structures, and several constructive phases, is: to what depth conduct the excavation? The answer depends on a number of factors, among these one of the most important is the a priori and reliable knowledge of what the subsoil can preserve. To this end, geophysics (if used in strong synergy with archaeological research) can help in the planning of time, depth, and modes of excavation also when the physical characteristics of the remains and their matrix are not ideal for archaeo-geophysical applications. This is the case of a great part of the archaeological sites in Henan, the cradle of the most important cultures in China and the seat of several capitals for more than two millennia. There, the high depth of buried remains covered by alluvial deposits and the building materials, mainly made by rammed earth, did not favor the use of geophysics. In this paper, we present and discuss the GPR and ERT prospection we conducted in Kaifeng (Henan, China), nearby a gate of the city walls dated to the Northern Song Dynasty. The integration of GPR and ERT provided useful information for the identification and characterization of archaeological remains buried at different depths. Actually, each geophysical technique, GPR frequency (used for the data acquisition) as well as each way to analyze and visualize the results (from radargrams to time slice) only provided partial information of little use if alone. The integration of the diverse techniques, data processing and visualization enabled us to optimize the penetration capability, the resolution for the detection of archaeological features and their interpretation. Finally, the results obtained from the GPR and ERT surveys were correlated with archaeological stratigraphy, available nearby the investigated area. This enabled us to further improve the interpretation of results from GPR and ERT survey and also to date the anthropogenic layers from Qing to Yuan Dynasty.

Spaceborne Multi-Temporal Synthetic Aperture Radar (SAR) Interferometry (MT-InSAR) has been a valuable tool in mapping motion phenomena in different scenarios. Recently, the capabilities of MT-InSAR for risk monitoring and preventive analysis of heritage sites have increasingly been exploited. Considering the limitations of conventional MT-InSAR techniques, in this study a two-step Tomography-based Persistent Scatterers (PS) Interferometry (Tomo-PSInSAR) approach is proposed for monitoring ground deformation and structural instabilities over the Ancient City Walls (Ming Dynasty) in Nanjing city, China. For the purpose of this study we utilized 26 Stripmap acquisitions from TerraSAR-X and TanDEM-X missions, spanning from May 2013 to February 2015. As a first step, regional-scale surface deformation rates on single PSs were derived (ranging from −40 to +5 mm/year) and used for identifying deformation hotspots as well as for the investigation of a potential correlation between urbanization and the occurrence of surface subsidence. As a second step, structural instability parameters of ancient walls (linear motion rates, non-linear motions and material thermodynamics) were estimated by an extended four-dimensional Tomo-PSInSAR model. The model applies a two-tier network strategy; that is, the detection of most reliable single PSs in the first-tier Delaunay triangulation network followed by the detection of remaining single PSs and double PSs on the second-tier local star network referring to single SPs extracted in the first-tier network. Consequently, a preliminary phase calibration relevant to the Atmospheric Phase Screen (APS) is not needed. Motion heterogeneities in the spatial domain, either caused by thermal kinetics or displacement trends, were also considered. This study underlines the potential of the proposed Tomo-PSInSAR solution for the monitoring and conservation of cultural heritage sites. The proposed approach offers a quantitative indicator to local authorities and planners for assessing potential damages as well as for the design of remediation activities.

Building footprint is a key indicator of urban structures and economic development. Automatic extraction of building footprint from very-high-resolution (VHR) remote sensing imagery, which is of great practical interest for various geospatial-related applications, is still a challenging task for complex textures, varying scales and shapes, and other confusing artificial objects. To alleviate these problems and improve the extraction accuracy, this study proposed a novel pure convolutional neural network called ConvBNet, which integrates ConvNeXt-XL with a fusing decoder and adopts deep supervision in the training stage for the middle stage. Two-stage training strategy, using weighted cross-entropy (CE) loss and mask CE loss, respectively, ensures the stable convergence and makes the network focus on the boundary region. The proposed model was tested on the Wuhan University (WHU) building dataset (publicly available) and one private Zhejiang building dataset. Compared with other state-of-the-art (SOTA) methods, ConvBNet achieved the best intersection over Union (IoU), 91.22% and 77.90% for the two datasets, which proves its good performance in the task of extracting buildings from VHR images.

Over the permafrost region of the Qinghai-Tibet Plateau, serious decorrelation has restricted the inteferometric synthetic aperture radar (InSAR) techniques in monitoring ground deformation and thaw-melt hazards. Improved understanding, quantification and prediction of the coherence evolution with time are key prerequirements for choosing the optimal observation time and band in such interferometric applications. In this letter, considering the main processes of degradation and the freeze-thaw cycle, a temporal decorrelation model including both the long-term and seasonal coherence factors is proposed. Multitemporal L-band PALSAR, C-band ASAR, and X-band TerraSAR were collected to validate the model's applicability. Coherence matrices of several typical features were estimated for the parameter inversion. Root-mean-square errors ranging from 0.048 to 0.152 showed good fitting results between the model predictions and the estimated interferometric coherences. The temporal decorrelation characteristics of the permafrost region at the three frequencies were successfully revealed. This letter could also facilitate applications of InSAR techniques in similar areas in other parts of the world.

This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran’s I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen ore-forming elements (Cu, Pb, Zn, W, Sn, Mo, F, Au, Fe₂O₃, etc.) in stream sediment samples in the Bayantala-Mingantu district, North China. The results showed that the OGE analysis performed at lower levels of scale greatly improved the quality of anomaly recognition: more than 80% of the known ore spots, no matter what their scales and mineral species, were predicted in less than 45% of the study area, and most of the ore spots falling outside the delineated anomalous regions occur nearby them. OGE can extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it outperforms other convolutional autoencoder models such as GAUGE in anomaly detection.