Conference Agenda

Under the background of city physical examination and assessment in territorial spatial planning, urban livability has become a focus of interest. Urban livability reflects residents' overall satisfaction with their living environment. Previous studies have been constrained by issues such as low data precision, coarse spatial scales, and limited practical applicability. To address these limitations, this study developed a refined livability evaluation framework by multi-source remote sensing data, with a primary emphasis on high-resolution domestic satellite imagery, including Gaofen (GF-1) and Ziyuan (ZY-3). Integrated with Suomi NPP night-time light data and socio-economic datasets, the research assessed four key dimensions, which were safety and resilience, residential comfort, recreation convenience, and quality and vitality in the city of Wuhan and Yibin at a detailed kilometer-grid scale. Results revealed distinct spatial patterns of urban livability of the two cities: Wuhan's central urban areas exhibited higher, more clustered livability, driven largely by quality and vitality, whereas Yibin showed a more fragmented pattern with strengths in recreation convenience but relative weaknesses in residential comfort and urban vitality. This study underscores the significant value of high-resolution, multi-source remote sensing data in enabling precise, spatially explicit livability analysis, thereby providing a scientific basis for targeted spatial planning and urban quality enhancement.

Dengue transmission in rapidly urbanising tropical cities is shaped by the combined influence of climate variability, urban morphology, and short-term surface water dynamics. This study develops a remote sensing and GIS-based framework to investigate the interaction between built-up density, waterlogging, and dengue incidence in Chennai from 2021 to 2023. Multi-source datasets, including Sentinel-2 imagery, NICFI high-resolution LULC, NDVI, and NDWI indices, Google Open Buildings footprints, IMD daily climate variables, and geocoded dengue case records, were integrated into a harmonised spatial grid for systematic analysis. Waterlogging-prone zones were delineated using a Sentinel-2 water-frequency method to capture the post-rainfall surface water accumulation rather than only persistent water bodies. Spatial clustering of dengue cases was examined using kernel density estimation, Global and Local Moran’s I, and Getis-Ord Gi*, revealing strong spatial autocorrelation and persistent hotspots in older, densely built neighbourhoods such as Kodambakkam, Adyar, Guindy, Saidapet, and Velachery, where compact built-up patterns and drainage limitations facilitate vector breeding. Peripheral areas showed weaker clustering and lower disease intensity. To assess the climatic influences, a Distributed Lag Non-linear Model (DLNM) was employed to quantify the delayed and non-linear effects of rainfall, maximum temperature, and minimum temperature on dengue incidence. Results showed notable lagged responses, with rainfall and minimum temperature exhibiting strong delayed associations aligned with mosquito development and viral incubation cycles. By integrating climatic, hydrological, and urban structural metrics, this study provides a replicable geospatial workflow for identifying micro-scale dengue-risk environments, supporting evidence-based vector-control strategies and climate-resilient urban planning in tropical cities.

Ticks are key vectors of human and animal disease, with Borrelia burgdorferi sensu stricto, the causative agent of Lyme disease, posing the greatest risk in North America. In Canada, Lyme disease cases are rising as the blacklegged tick (Ixodes scapularis) expands northward, driven by climate change, land cover shifts, and host movement. The Kingston, Frontenac, Lennox and Addington (KFL&A) region is a well-established hotspot, highlighting the importance of mechanistic models that realistically represent heterogeneous environmental drivers of transmission.

This study integrates multi-sensor Earth observation (MODIS, GEDI, Landsat) with climate, habitat, and ecological data to improve mechanistic tick phenology models. A hierarchical framework incorporates microclimate, landscape, and regional variables, enabling assessment of how sensor type, spatial resolution, and environmental gradients influence seasonal tick activity predictions. Model calibration and validation use field-collected tick and pathogen data, supplemented by citizen science observations.

By systematically linking EO to disease modeling, this approach improves the representation of environmental drivers, enhances predictive performance, and supports public health planning. The framework is transferable to other vector-borne diseases, advancing the integration of remote sensing into epidemiological forecasting at regional to national scales.

Illegal waste disposal sites pose significant ecological and public-health risks yet remain difficult to track with traditional field inspections. We propose a multi-agent detection framework that fuses textural, spectral, and contextual cues from medium-resolution satellite imagery for this work. Three specialised agents - Waste-Pile, Road, and Industry detectors - are implemented as YOLO (You Only Look Once) convolutional models that generate partial hypotheses, which are then hierarchically aggregated through rule weights learned from expert-labelled samples. The system provides an interpretable set of object relations, allowing regulators to trace how individual cues contribute to the final decision. The method was validated on an independent test area near Taromske (Dnipropetrovsk region, Ukraine) and corroborated by ground surveys. Joint aggregation raised the posterior probability of the primary target cluster from 0.27 (single-detector confidence) to 0.91, while maintaining robustness to label noise and heterogeneous sensor characteristics. Compared with conventional CNN baselines, the proposed approach delivers three key advantages: explicit explainability of outputs, transferability to 10 m spatial resolution without extensive retraining, and seamless integration of heterogeneous evidence sources. The proposed framework can serve as a cost-effective backbone for regional and national waste-monitoring systems. Future work will focus on near-real-time processing of Sentinel-2 time series, incorporation of hyperspectral and thermal methane indicators to assess remediation stages, and extension of the array of features to other anthropogenic disturbances such as open-pit mining and construction debris.

Based on traditional PS-InSAR technology, this study proposes a building elevation estimation method based on long and short baseline iteration. It utilizes long-temporal SAR images for multiple iterations to calculate building heights, which are used as prior information. Combined with the Interferometric Point Target Analysis (IPTA) method, it inverts building deformation information. The K-means clustering method is employed for PS point clustering analysis, classifying PS points with similar deformation trends and mapping them to buildings. A building safety risk assessment system is established, which comprehensively evaluates the cumulative deformation amount and deformation rate of both the building structure and its foundation. In this paper, the feasibility of the above method is verified by an example. The deformation of 9442 buildings is extracted in the study area, of which 245 buildings are in a high security risk state, and 2 buildings are in a high security risk state. Through this study, it can provide comprehensive auxiliary decision-making reference data covering macro wide-area and micro single buildings for urban construction management departments.