Conference Agenda

High-resolution (HR) satellite imagery is essential for urban monitoring and disaster management, but its use is constrained by high cost and limited accessibility. Super-resolution (SR) offers an efficient alternative by reconstructing high-quality images from low-resolution (LR) inputs, making large-scale geospatial analysis more feasible. We propose the Multi-Attention Pyramid Super-Resolution Network (MAPSRNet), which delivers two main innovations:

1. A multi-attention model that integrates a Pyramid Vision Transformer for long-range spatial dependencies with a cross-channel Involution+ module to enhance feature interactions, generating SR images with superior structural preservation and sharper boundaries.

2. The first SR network to surpass the performance of original HR images in downstream tasks, demonstrated through building detection with a ConvNeXtV2 backbone and U-Net decoder. MAPSRNet reduces false positives and negatives and, across multiple datasets, exceeds HR performance in IoU, F1-score, and overall accuracy.

Extensive experiments on the Massachusetts building dataset, the Wuhan University building dataset, and the Waterloo building datasets confirm that MAPSRNet consistently outperforms representative SR methods in both image fidelity (PSNR, SSIM) and task-level metrics. Its ability to preserve fine structural details, suppress background noise, and learn resolution-invariant features through multi-resolution training makes the reconstructed images more task-aware than raw HR data. Beyond buildings, this flexibility suggests strong potential for generalization to other land-cover classes such as roads, vegetation, and water bodies.

These results establish MAPSRNet as a cost-effective alternative to HR acquisitions and a milestone in task-driven SR research, advancing both image reconstruction and downstream geospatial analysis.

High revisit-rate SAR constellations generate large volumes of imagery that require consistent geolocation accuracy to support applications such as change detection and interferometry. However, variations in orbit determination, attitude knowledge, and external factors such as Global Navigation Satellite System (GNSS) interference can introduce geolocation errors that vary across acquisitions, making large-scale validation challenging. This study presents an automated approach to detect and quantify geolocation offsets in ICEYE SAR imagery by aligning orthorectified scenes with reference images using feature-based matching and correlation-based refinement. The method is validated against independently derived absolute geolocation measurements from corner reflector calibration sites in the United States, Canada, Australia, and Poland. Evaluation across 726 acquisitions demonstrates strong agreement with reference measurements, achieving an overall root-mean-square error (RMSE) of 1.39 m, with RMSE values of 1.18 m for Spotlight mode and 1.93 m for Stripmap mode. Operational applicability is demonstrated through large-scale acquisition campaigns, including nationwide Stripmap coverage over Japan and coherent image stack analysis. The results show that the proposed method can reliably estimate geolocation offsets, detect anomalies, and monitor geometric consistency across large SAR archives, providing a practical and scalable solution for automated geolocation quality control in micro-satellite SAR constellations.

Urbanisation and land-use change increase the demand for temporally consistent urban maps from high-resolution Earth observation imagery. A key obstacle is label ageing: benchmark annotations are often years older than current true orthophotos (TOP), causing semantic and geometric mismatches (e.g., demolished/new buildings, shifted vegetation boundaries) that degrade supervised learning, calibration, and transfer. This paper presents a probabilistic, quality-aware segmentation framework based on a compact U-Net. Legacy annotations are converted into edge-adaptive soft labels to encode boundary uncertainty. A HELIX-derived per-pixel supervision quality score Q is computed and integrated as a weight in a Q-weighted Kullback--Leibler objective with an agreement-focal component, reducing the influence of unreliable or outdated regions. Global temperature scaling is then applied to obtain calibrated per-class probability fields with comparable confidence magnitudes. Experiments on ISPRS Potsdam and Vaihingen combined with recent (2024) TOPs evaluate temporal transfer (archival supervision vs. updated imagery of the same area) and spatial transfer (cross-city application). Finally, calibrated probability fields are used to derive probabilistic semantic transitions and temporal reliability scores, supporting uncertainty-aware mapping of urban change such as construction, sealing, and vegetation loss.

Survivorship bias refers to the fact that conclusions are drawn from a non-representative sample limited to cases that have survived a selection process. This article shows that this bias affects scientific literature, which tends to select successful experiments and hide failures. Remote sensing, like other data-driven sciences, is affected by survivorship bias, making it difficult to have a clear idea of the data's and methods' actual potential and limitations. A typology of failure causes is proposed to encourage critical reading of the bibliography, and perspectives are outlined to overcome survivorship bias by improving practices within the academic and industrial remote sensing communities.

Satellite remote sensing is inherently constrained by a fundamental spatio-temporal trade-off by physical sensor limitations. Super-Resolution (SR) techniques are required to overcome these constraints and obtain high-resolution time-series data. However, Single Image Super-Resolution (SISR) provides insufficient information for robust restoration. To address this, Reference-Based Super-Resolution (Ref-SR), which utilizes a high-resolution (HR) reference (Ref) image, has been investigated. Nonetheless, Ref-SR introduces the challenge of reference misuse, stemming from the temporal mismatch (or inconsistency) between the target low-resolution (LR) image (e.g., clouds, seasonal changes) and the Ref image (often a long-term median composite). To address this reference misuse problem, this study proposes an adaptive Ref-SR framework that incorporates a similarity weight map derived from the LR and Ref information. This weight map is computed solely from the pixel-wise similarity between the LR and Ref inputs, requiring no ground truth HR, and functions as a gating mechanism. This allows the network to dynamically control Ref reliability, guiding it to suppress Ref influence in mismatched regions and leverage its textures in similar ones. Validation experiments using Sentinel-2 data (LR 240m, Ref/HR 60m) demonstrate that the proposed method achieves significant performance improvements over SISR in both spatial (Peak Signal-to-Noise Ratio, Structural Similarity Index) and spectral (Spectral Angle Mapper, Error Relative Global Dimensionless Synthesis) metrics. Furthermore, qualitative analysis confirms that the framework effectively suppresses artifacts caused by the blind injection of Ref textures in inconsistent areas. This framework could contribute to the future fusion and quality enhancement of heterogeneous LR sensor data, such as GOCI-II.

The Copernicus Land Monitoring Service (https://land.copernicus.eu) has been providing geophysical data derived from Earth Observation (EO) at a global scale for several decades. This global dataset includes temperature and reflectance, vegetation, soil moisture, snow and water bodies variables. To ensure the quality of these dataset, yearly validation assessment is performed. The collection and processing of ground data for the purpose of validating Copernicus products represents in itself a huge task. In 2018, the European Commission (EC) has established a new service to ensure the independent production of these data: Ground-Based Observations for Validation (GBOV) https://gbov.land.copernicus.eu). The prime objective of GBOV has been for the last 8 years, to provide high-quality validation data for seven Copernicus Land Monitoring Service core products:

• Top Of Canopy Reflectance (TOC-R),

• Albedo (ALB),

• Leaf Area Index (LAI),

• Fraction of Absorbed Photosynthetically Available Radiation (FAPAR),

• Fraction of Vegetation Cover (FCOVER)

• Surface Soil Moisture (SSM) and

• Land Surface Temperature (LST).

In its third phase, new product have been included to support the growing Copernicus land products portfolio, namely:

•GPP and NPP

•Phenology

•Evapotranspiration

GBOV includes three components in the service:

•Component 1: consists of using data from existing in situ networks to generate EO validation datasets. Multi-year ground-based observations of high relevance for EO are collected from these global networks.

•Component 2: consists of upgrading existing monitoring sites with new instrumentation or establishing entirely new monitoring sites to close thematic or geographic gaps.

•Component 3: deals with data distribution of the validation dataset to the user community.