Conference Agenda

Southwestern China’s complex terrain and climate make landslides frequent, especially along highways where numerous high, steep artificial slopes are formed during construction. These slopes often deform or fail within 1–2 rainy seasons due to intricate geology, severely affecting construction and infrastructure safety. An automated, real-time monitoring and early-warning system is therefore urgently needed.

Conventional techniques (leveling, GPS, crack meters) are limited by small coverage, low efficiency, high cost, and inability to detect regional or hidden deformations. Spaceborne InSAR offers wide-area, high-precision, all-weather monitoring but faces severe decorrelation noise from dense vegetation and atmospheric delay errors in mountainous regions.

This study developed advanced InSAR methods for artificial slopes along the Huali Highway (G4216) in Yunnan Province. Using TCPInSAR and >240 Sentinel-1 images (2015–2025), we retrieved surface deformation throughout pre-construction, construction, and post-construction phases. To overcome local challenges, two novel correction approaches were proposed: (1) a noise-reduction method based on spatial correlation estimation of deformation signals, effectively suppressing vegetation-induced decorrelation; and (2) an atmospheric correction technique using Singular Spectrum Analysis (SSA), significantly reducing delays caused by complex weather.

Results show the improved InSAR system successfully detected multiple deformation zones along the corridor and provided reliable early warnings for safety management. By addressing key technical bottlenecks, this work validates the practicality and effectiveness of advanced InSAR for automated slope stability monitoring in geologically and environmentally complex regions, offering valuable reference for similar large-scale infrastructure projects.

Meteorological control of L-band forest backscatter from the BorealScat-2 radar tower

How strongly do weather conditions imprint on L-band radar signals from forests at sub-daily time scales? This question is investigated using the BorealScat-2 tower experiment in the Svartberget Experimental Forest (northern Sweden). The system acquires fully polarimetric, tomographic radar data at P, UHF and L band every 30 minutes, providing height-resolved backscatter profiles from the ground, through the trunk zone, into the upper canopy. Within the shared footprint, an ICOS flux mast delivers continuous measurements of CO₂, water vapour and energy fluxes, together with radiation, vapour pressure deficit (VPD), temperature, wind and precipitation. Sap-flow sensors, dendrometers and soil water probes further characterise water storage and transport in trees and soils, offering an unusually detailed description of forest water dynamics.

The study will focus on L-band backscatter during late spring and summer, quantifying how diurnal amplitude, phase and vertical centre-of-mass in different height zones and polarisations relate to VPD, temperature, radiation and rainfall. It will specifically assess the relative roles of atmospheric demand, canopy wetness and soil water status in driving sub-daily L-band variability, and examine differences between co- and cross-polarised channels and between structural layers. Overall, the study aims to provide process-based insight into how specific meteorological drivers control sub-daily L-band radar variability in boreal forests, supporting the interpretation and modelling of future vegetation radar missions.

The digital elevation model (DEM) based on synthetic aperture radar interferometry (SAR, InSAR) technology have become an important data source for large-scale topographic mapping, but their characteristics vary with satellite systems and methodologies. In this paper, we conduct the cross-validation for the first time to compare the LuTan-1 raw DEM (LT-1 RDEM) and GaoFen-7 (GF-7) satellite laser altimetry data. Besides, we compared the penetration capabilities of SAR satellites including C-band SRTM and X-band TanDEM-X. The optically derived ZiYuan-3 (ZY-3) DEM was also included for multi-source cross-validation. Taking Guyuan County, Hebei Province, China (including four landform types: plains, tablelands, hills, and mountains) as the study area, we introduced GF-7 laser altimetry points (LAPs) as the verification benchmark to cross-validate the elevation accuracy of LT-1 RDEM, SRTM, TanDEM-X DEM (TanDEM), and ZY-3 DEM. The results indicate that: (1) Topographic relief has a significant impact on accuracy, and the RMSE of the DEMs in the study area generally increases sequentially with the intensification of topographic relief; (2) Benefiting from the 10-meter spatial resolution, LT-1 RDEM performs best in detail representation; (3) In terms of mean height error, LT-1 RDEM exhibits a general negative bias, confirming the stronger penetration capability of the L-band, and its elevation values may be closer to the true ground surface; (4) The RMSE of LT-1 RDEM in the study area is 1.958m, slightly larger than TanDEM’s 1.65m, but in fact, the accuracy of TanDEM as a digital surface model (DSM) may be systematically overestimated by laser altimetry data.

This study evaluates the operational capability of the Chinese LuTan-1 (LT-1) L-band SAR constellation for monitoring the Hong Kong–Zhuhai–Macao Bridge (HZMB), a representative sea-crossing bridge under a complex subtropical marine climate. Leveraging the advantages of L-band SAR—including strong resistance to decorrelation and a spatial resolution of up to 3 meters—we applied the Small Baseline Subset (SBAS) technique to 47 ascending and descending orbital images. To the best of our knowledge, this represents one of the first comprehensive deformation studies of the HZMB using the LT-1 constellation. A key aspect of our methodology is the cross-validation between multi-orbit datasets, which confirmed both the reliability of the measurements and the complementary distribution of coherent points due to SAR imaging geometry. The results indicate overall structural stability of the HZMB, with the maximum deformation localized at the Jianghai Navigation Bridge, showing a Line-of-Sight (LOS) displacement rate of –4.3 mm/yr. In contrast, the two artificial islands exhibited minor deformation, with LOS rates not exceeding –3.0 mm/yr. These findings validate LT-1 as a powerful and reliable tool for the operational health monitoring of large-scale coastal infrastructure.

LuTan-1 (LT-1) satellites have been launched for about 4 years. About 771,282 images have been distributed to the users of China till 29th October, 2025. Main application purpose of LT-1 is geohazard monitoring and geoinformation production. Interferometric capability is the primary consideration for LT-1. In this paper, we assessed the interferometric applications in the natural resource monitoring industry. First, we overviewed the status of LT-1, the main interferometric products were introduced as S2A, S2B, S3A, S4A, S5A, S5B and S5C. They are geometrically calibrated single look complex (SLC) image, interferometrically calibrated SLC, differential interferometric synthetic aperture radar (SAR, InSAR, DInSAR) products, stacking, MTInSAR, digital orthorectified image, and digital surface model, respectively. S2A are generated after geometric calibration, the geometric accuracy is about 1.53 after calibration. The baseline is then calibrated for helix bistatic formation data and generate S2B whose accuracy is better than 0.96 m. S3A, S4A and S5A are all used for deformation monitoring, the accuracy values of them are 2.7 mm, 8.6 mm/yr, and 3.7 mm. Geometric accuracy of S5B is 12.5 m, and the height accuracy of S5C is better than 4.7 m. More than 330 geohazards were detected in Guangdong province. The geohazards recognition rate in the field working stage increase from 28% to 47.24%. Even a prediction has been made to avoid disaster for a family and saved 3 people. The application effectiveness has been validated through those years.

The influence of soil moisture change on interference phase information is fully taken into account for accurate deformation monitoring in this research. Especially the effects have more prominent contribution to L-band SAR data. In order to obtain high-precision surface deformation information over agricultural area, the interference phase component caused by soil moisture change should be considered, and the optimal processing of interference phase information is achieved. The reliable interference phase information characterizing the surface deformation details is obtained, thus the natural surface deformation information with high precision can be achieved. Firstly, the penetration depth of different band SAR for agricultural soil was analyzed and simulated. And the sensitivity between penetration depth variation and different band SAR signals were discussed. The fact of soil moisture changes for interferometric phase contribution is confirmed, which provided the foundation for reliable deformation montoring considering the soil moisture variation effects, especially for L-band SAR data. The periodic irrigation for the wheat fields will induce soil moisture variation, which may result in the penetration depth change for radar electromagnetic wave. Therefore, the phase component was derived by the variation of soil moisture over the wheat fields. Multi-temporal Lutan-1 SAR data were acquired over ShanDong agricultural plain in China. The obvious ‘deformation details’ induced by the soil moisture change were acquired over the agricultural area, which demonstrated the effect of soil moisture variation for interference phase. Therefore, the accurate deformation details over agricultural area can be obtained by the combination of soil moisture information.

Asteroid exploration is crucial for understanding the solar system’s origin, but establishing a precise body-fixed coordinate system—relying on accurate rotation parameters—remains challenging. Conventional methods like ground-based light curve inversion often lack precision: for example, it yielded ±2° errors for Ceres’ pole and ±10° for Vesta’s, failing to meet demands for topographic mapping and navigation. This study proposes a refinement method combining stereo intersection angle optimization and grayscale threshold masking. First, using the camera’s interior orientation parameters and tie point coordinates, relative orientation of stereo image pairs is conducted to build a stereo model, followed by forward intersection to calculate intersection angles. Only pairs with favorable geometry (intersection angle >5°) are retained to avoid large position errors from nearly parallel sightlines. Second, a grayscale-based binary mask is created to separate the asteroid from the deep-space background, eliminating spurious edge features that cause mismatches; the SIFT algorithm then extracts and matches features exclusively within the masked region. Finally, an “exhaustive search” iteratively refines rotation parameters using optimized matched points. Validated on 127 Hayabusa2 ONC-T images of asteroid Ryugu (captured July 10, 2018, 2.11m/pixel), the method reduced 5,174 initial candidate pairs to 1,454 valid ones (137,191 matched points). After 4 iterations, refined parameters were RA=96.5° and Dec=-66.4°, with minimal errors (δRA=0.069°, δDec=0.0126°) against reference values (RA=96.431°, Dec=-66.387°). Compared to methods without the two strategies, mismatches dropped from 14,949 to 7,369, and forward intersection residuals decreased. Future work will integrate initial parameters into a bundle adjustment model for further refinement.

The lunar polar regions have emerged as core targets in lunar exploration, primarily due to the potential water ice resources stored within their permanently shadowed areas. However, the complex terrain and extreme illumination conditions in these polar regions present significant challenges to the navigation of lunar rovers—systems that previously relied on dead reckoning and visual matching techniques. To address this, active 3D sensors such as LiDAR will be integrated into future exploration missions.Simultaneous Localization and Mapping (SLAM) based on multi-sensor fusion via factor graphs can significantly enhance the localization robustness of rovers on the lunar surface. In this context, we propose the Lunar Scene Recognition Adaptive SLAM (LSRA-SLAM) method: a framework that leverages environment-aware pre-training to dynamically adjust factor-graph weights, thereby achieving more consistent fusion of stereo camera, LiDAR, and IMU measurements across diverse lunar scenarios. We also introduce a reinforcement learning-based online training strategy, which enables the network to robustly learn from the system's dynamic behaviors. Simulated experiments validate the effectiveness of the proposed LSRA-SLAM method.

Accurate crater mapping in lunar permanently shadowed regions (PSRs) is hindered by extreme low-light and low-resolution imagery. We present YOLOLens2.0, a unified, end-to-end deep learning framework designed for high-fidelity crater detection and terrain reconstruction in these challenging environments. The architecture integrates a Dense-Residual-Connected Transformer

(DRCT) for multimodal super-resolution (SR) with a YOLO-derived detection module and an affine calibrator to ensure geometric consistency at meter scale. Our framework exploits a bidirectional synergy where SR enhances feature discriminability for detection, while detection-driven supervision refines structural reconstruction. Validation on Kaguya data demonstrates a significant performance leap, achieving a Recall of 89.20% and an mAP@50 of 0.844 an improvement of over 33 percentage points in recall compared to the original YOLOLens. Out-of-distribution validation on ShadowCam imagery, performed without fine-tuning, confirms the model’s robustness and scalability. The framework successfully preserves quantitative elevation fidelity and supports detailed morphometric analyses, including the extraction of the crater size-frequency distributions (SFDs) that align with theoretical lunar production functions. YOLOLens2.0 provides a scalable, high-precision methodology for planetary mapping, offering critical insights for lunar surface evolution studies and future exploration missions.

Reliable localization across the full orbit-descent-ground chain in planetary exploration remains difficult because extreme differences in altitude, viewing geometry, resolution, and illumination cause cross-view image matching to fail. Traditional keypoint pipelines and unified Structure-from-Motion (SfM) struggle to establish robust correspondences across these heterogeneous Satellite-Descent-Ground datasets due to severe domain gaps.

To overcome these limitations, we propose a novel framework based on a joint semantic-geometric optimization paradigm. Rather than forcing a unified SfM pipeline across drastically different viewpoints, our method leverages independent intra-domain SfM outputs and telemetry data as structural priors. We introduce a differentiable rendering approach that tightly couples the optimization of 3D Gaussian Splatting (3DGS) scene parameters with learnable camera extrinsics. Furthermore, by integrating high-level semantic epipolar constraints derived from foundation models, our method dynamically refines initial cross-domain pose estimates during the rasterization loop. This joint formulation effectively bypasses the fragility of low-level pixel matching, enabling accurate and robust alignment across the vast baselines inherent to multi-stage planetary exploration image sequences.

Mars topographic data are crucial for quantitatively characterizing the Martian surface, supporting exploration missions, and enabling scientific study of surface processes. Photogrammetric processing of Mars orbital imagery is the most representative method for generating 3D terrain models, with bundle adjustment (BA) serving as the key step for mitigating inconsistencies in overlapping regions of different orbital images and further improving the spatial accuracy of the resulting DTMs. However, due to the texture-less surface of Mars and the absence of ground control points, the stability of BA is often compromised. Impact craters, which are prevalent on the Marian surface, have been utilized as an important semantic prior in various image analysis applications. They can also be used to assist the BA process for precision topographic mapping of the Martian surface.

This study introduces a novel BA method assisted by robust crater graph features to address this. The approach involves: (1) extracting craters using a deep learning model (YOLOv5) and constructing a stable graph structure via a minimum spanning tree; (2) establishing crater correspondences across different images based on graph features to generate robust tie points; and (3) formulating a strengthened BA equation with constraints from the graph's angular and edge relationships to mitigate geometric inconsistencies.

Experimental results indicate that the proposed method provides an effective solution for high-precision 3D mapping from Martian surface imagery with limited textures and significant illumination variation. By incorporating crater graph features, it enhances the precision and stability of BA, yielding high-precision topographic mapping results for various applications.

Surface roughness influences image contrast by altering illumination, which depends on the surface slope. We conducted Monte Carlo simulations of rough surfaces under both directly illuminated and secondary-illuminated lunar conditions. Our results indicate that PSR secondary illumination yields significantly lower contrast, characterized by soft, diffuse shading and negligible shadow fraction.

With the acceleration of global urbanization, dynamic change detection of urban buildings is vital for urban planning, resource management, and public safety. Traditional bi-temporal remote sensing-based methods fail to capture gradual building evolution and are prone to noise-induced missed detections and false alarms. While multi-temporal imagery provides continuous temporal information, its sequential and high-dimensional nature poses greater challenges. Existing deep learning models like CNNs excel at spatial feature extraction but lack temporal modeling, while LSTM/ConvLSTM struggles with spatial detail preservation and small-target recognition.

To address issues including insufficient temporal modeling, channel redundancy, weakened spatial attention, and small-target loss, this study proposes the Dual Attention-coupled ConvLSTM Network (DAL-UNet). Its encoder embeds a dual attention module: channel attention selects change-related features and suppresses redundancy, while spatial attention enhances key region responses to improve building edge and small-target discrimination. A fully convolutional LSTM module models temporal evolution while preserving spatial topology. The decoder adopts a dual-branch multi-task framework to optimize change feature upsampling and semantic segmentation, enhancing subtle change perception and spatial detail restoration.

Experiments on the SpaceNet7 dataset show DAL-UNet outperforms state-of-the-art methods, with maximum improvements of 13.04% in F1-score, 1.32% in Precision, and 16.52% in Kappa coefficient. It performs exceptionally in high-rise shadow areas and dense small-target regions, reducing shadow interference via attention mechanisms and alleviating class imbalance through class-weighted loss.

In the aftermath of a disaster, whether natural, industrial, or war-related, a rapid and accurate assessment of building damage is crucial for rescue forces to conduct an effective emergency response. Very high-resolution satellite imagery enables such assessments and serves as an important indicator for understanding the scale of destruction, supporting time-critical rescue operations, and guiding resource allocation. While deep learning models have shown promising results in automating building damage assessment (BDA) from pre- and post-disaster optical satellite imagery, they often fail to generalize to new disasters due to domain shifts. This paper studies the challenge of rapid domain adaptation for BDA in the context of the war in Ukraine. We create a new, challenging dataset annotated with damage grades across six cities in Ukraine, using pre- and post-disaster optical imagery. To facilitate rapid adaptation, we propose an efficient fine-tuning workflow using Low-Rank Adaptation. Our experiments show that this approach substantially improves performance in both out-of-domain and in-domain settings, presenting a practical and data-efficient study for deploying BDA models in time-critical emergency scenarios.

This study examines district-level urban expansion and ecological change in the Hills Shire LGA using multitemporal Landsat imagery, Shannon’s entropy, RSEI, and hotspot analysis to identify spatial patterns of growth and environmental stress.

Urban sprawl, characterized by land discontinuity, low population density, and inefficient land use, hinders sustainable urbanization, particularly in rapidly growing regions such as Bulacan, Philippines. This phenomenon places strain on existing infrastructure, contributes to environmental degradation, and exacerbates socio-economic disparities. While previous studies have analyzed urban sprawl, these often neglect the integration of socio-economic factors, thereby reducing the accuracy of their analysis and policy relevance for developing regions. This research seeks to analyze urban sprawl patterns within Bulacan through the integration of socio-economic variables and identify key factors driving this sprawl. The study employs urban sprawl analysis, using the Multidimensional Urban Sprawl Index (USI) to assess land discontinuity, population density, and land use efficiency. Additional analysis using fractal analysis and factor analysis through Geodetector was also employed. The study found a positive shift toward more efficient, compact growth in Bulacan from 2005 to 2020, though mild and severe sprawl remain ongoing challenges. Fractal analysis revealed that complex urban forms encourage infill, while open areas are prone to leapfrog development. Land use benefit and road access consistently drove sprawl, with key factors like population and proximity to the city center changing over time. The study recommends stricter enforcement of zoning regulations to mitigate fragmented growth and the integration of additional socio-economic indicators (e.g., GDP, employment rates, and land values) into future analysis.

This study demonstrated that a two-stage architecture—comprising a segmentation model followed by a classification model—is effective for embankment extraction. By constructing a large, wide-area training corpus from medium-resolution SPOT imagery, transfer learning to higher-resolution satellites (e.g., Pleiades) was readily achieved. For operational use, exhaustively proposing candidates with the AI model and inserting a brief human check (embankment/non-embankment) per candidate can reduce false positives while limiting missed detections, making the approach sufficiently practical for deployment.

Land-sea segmentation is crucial for tasks such as marine target detection and coastline extraction in remote sensing imagery. However, complex and diverse background environments and land-sea boundaries can easily lead to inaccurate segmentation. To address this issue, a high-precision land-sea segmentation model based on the deep Otsu method is proposed. This method first utilizes our proposed remote sensing image texture enhancement algorithm based on Retinex theory and the Canny operator to enhance the remote sensing image and its edge information, further improving the segmentation accuracy of the land-sea boundary. Then, we combine deep learning concepts, the maximum inter-class variance method, and our proposed density space clustering method based on the difference innovation optimization algorithm to propose a deep maximum inter-class variance method for segmenting the ocean and land in the image. Simultaneously, an adaptive multi-scale fragmentation region removal method is proposed to remove small, fragmented regions extracted during the segmentation process. Experimental results show that the proposed method achieves an overall prediction accuracy of 98.41% and an average intersection-union ratio of 96.07%, demonstrating its ability to effectively perform land-sea segmentation tasks.

Low-resolution imagery is a major constraint for remote sensing tasks (e.g., urban land cover detection) where accurate classification of buildings, roads, vegetation, and small objects is required. Deep learning-based segmentation models are highly sensitive to image quality, resulting in degraded performance on low-resolution inputs. Super-Resolution (SR) techniques offer a promising solution by enhancing image fidelity to support downstream tasks. This work applied MAPSRNet, a Multi-Attention Pyramid SR Network to aerial images used for multi-class land cover detection. Evaluated on the ISPRS Potsdam dataset, MAPSRNet achieves state-of-the-art SR performance with PSNR of 32.92 dB and SSIM of 0.87, outperforming existing methods such as SRCNN (31.54 dB, 0.83) and DRRN (31.03 dB, 0.82) while maintaining competitive inference speed. Beyond image quality, MAPSRNet significantly improves multi-class land cover segmentation when integrated with a ConvNeXtV2-based U-Net, achieving an overall accuracy of 80.60%, mean IoU of 62.54%, and FwIoU of 68.34%, surpassing not only low-resolution inputs (Overall Accuracy: 65.28%, mIoU: 40.20%, FwIoU: 50.12%) but also high-resolution(HR) ones (Overall Accuracy: 80.50%, mIoU: 62.40%, FwIoU: 68.01%), especially in certain classes such as impervious surface and clutter. These results demonstrate that perceptual and structural fidelity, rather than pixel-level similarity, can drive superior performance in urban land cover segmentation. MAPSRNet offers a practical solution for scenarios where HR imagery is limited or unavailable, highlighting its potential for large-scale remote sensing applications.