Conference Agenda

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.