Conference Agenda

This study takes into account the heterogeneity of regional development stages and the local development context, systematically explores the relationship between localization and sustainable development, and constructs a quantifiable localized SDGs assessment model for China. An empirical analysis of the multi-level SDGs evaluation system was conducted. To address the challenges posed by heterogeneous multi-source data in the assessment process, a composite Key Performance Indicator (KPI) screening model based on Random Forest and Hyperlink-Induced Topic Search (HITS) was proposed, enhancing the scientific rigor and efficiency of localized SDGs monitoring and evaluation.

This paper is mainly based on the experience of Jiaxing City, which has done a good job in the investigation and monitoring work in China, to inspire the investigation and monitoring work in Beijing, and to provide technical support for supporting the investigation and monitoring work in Beijing to achieve the goal of "one inspection, multi-purpose, integration and sharing". Through research, the four databases of new basic surveying and mapping, land change survey, urban land space monitoring, and land space planning are integrated, and the integration of content indicators, survey methods, collection and storage, management and sharing is realized.

Accurate extraction of water bodies from remote sensing imagery is essential for environmental monitoring, water resource management, and hydrological applications. However, the performance of deep learning models for water segmentation depends on the availability of representative datasets that capture diverse environmental and spectral conditions, particularly in tropical and semi-arid regions that remain underrepresented in existing datasets. This study presents the Pernambuco Waterbody Dataset (PWD), a multi-source dataset comprising aerial and satellite remote sensing imagery for water-body segmentation. The dataset covers the state of Pernambuco, Brazil, including tropical and semi-arid environments associated with the Atlantic Forest and Caatinga biomes. The dataset includes high-resolution aerial imagery (0.5 m) from the Pernambuco Tridimensional Program (PE3D) and Sentinel-2A imagery (10 m), with manually annotated water bodies generated by cartographic specialists. The dataset was constructed through data acquisition, preprocessing, manual annotation, mask generation, patch extraction (512 × 512 pixels), and division into training, validation, and test subsets. The first version includes 51,743 aerial patches and 15,321 Sentinel-2A patches. To validate the dataset, U-Net, U-Net++, and DeepLabV3+ architectures with ResNet and EfficientNet backbones were evaluated using Recall, Precision, F1-score, and IoU metrics. The best performance was achieved by U-Net++ (ResNet34) for aerial imagery (IoU 0.946) and U-Net (ResNet34) for Sentinel-2A imagery (IoU 0.871). Overall, the proposed dataset provides a robust benchmark for advancing deep learning-based water body extraction using multi-source remote sensing data.

Multispectral satellite imagery provides an essential source of information for monitoring environmental transformations, yet robust unsupervised change detection remains challenging due to radiometric variability and seasonal dynamics. At the same time, supervised approaches based on Deep Learning are often constrained by the need for computationally expensive accelerated hardware and the limited availability of high-quality annotated datasets. This work introduces a framework based on the Bitemporal Spatial Autocorrelation (BSAC) matrix, that rather than relying on pixel-wise spectral differencing or data-intensive Deep Learning models, it is designed to quantify structural changes by evaluating the symmetry properties of the spatial autocorrelation across multiple spatial lags. Three complementary metrics are derived from the BSAC representation: a binary change/no-change trigger that identifies structural discontinuities, an asymmetry magnitude that measures the intensity of change, and a normalized Symmetry Index obtained via singular value decomposition to characterize the geometric coherence of the correlation structure.

The methodology is applied to Sentinel-2 imagery of Lake Fanaco (Sicily, Italy), which experienced severe desiccation during the 2024 drought. Experiments conducted using NDWI and NDVI confirm the index-agnostic nature of the framework, capturing both

hydrological contraction and vegetation stress. Comparison with an unsupervised K-means segmentation baseline shows strong spatial agreement in identifying the affected areas. Thanks to its unsupervised formulation and near-linear computational complexity, the BSAC framework represents a scalable and interpretable approach for operational change monitoring in Earth observation.

Accurate and rapid burned-area mapping is essential for assessing the ecological impacts of forest fires and supporting post-fire recovery efforts. Traditional pixel-based methods often suffer from limited accuracy due to spectral confusion, topographic effects, and reliance on empirical thresholds. Although deep learning models such as U-Net, DeepLab, and SegFormer improve spatial precision, their operational scalability is constrained by the need for extensive labeled data and regional retraining.

This study introduces a zero-shot burned area mapping approach using the Segment Anything Model (SAM) with Sentinel-2 imagery. SAM, trained on over a billion masks, enables prompt-based segmentation without task-specific training. Composite inputs derived from NBR, NBR2, and NDVI indices were generated and fed into SAM, followed by testing multiple pre-processing, post-processing, and hyperparameter configurations.

Results show that multi-scale settings (crop_n_layers = 2) significantly enhance boundary continuity and geometric accuracy. The method achieved IoU values of 0.89 (Bursa) and 0.87 (Çanakkale), with corresponding F1 scores of 0.94 and 0.92 performances comparable to, and in some cases exceeding, supervised models. Integrating spectral index composites further reduced boundary fragmentation and improved discrimination between burned and unburned surfaces.

Overall, the proposed framework eliminates dependence on manual labeling, offering a fast, scalable, and cost-effective solution adaptable to diverse ecosystems and sensor conditions. The study demonstrates one of the first systematic applications of SAM for burned-area detection, highlighting its strong potential for zero-shot environmental monitoring and rapid post-fire assessment.

In many regions of the world, especially those affected by armed conflicts, urbanization, or intensive environmental transformations, a high dynamic of land cover and land use changes is observed. Reliable monitoring of these processes requires the application of classification methods that ensure both high thematic accuracy and temporal consistency. This paper presents a multitemporal classification methodology based on Sentinel-2 optical data and machine learning models.

The research was conducted for the city of Sievierodonetsk (Luhansk Oblast, Ukraine) – an area that suffered significant destruction in 2022 as a result of military operations. The aim of the analysis was to identify land use changes in the years 2021-2025 using three classifiers: k-Nearest Neighbors (kNN), Random Forest (RF), and Gradient Boosting Classifier (GBC), combined into an ensemble system based on dynamic confidence weighting.

Quality assessment using the recall metric showed that the fusion method outperformed individual classifiers, achieving average values of 0.87-0.96, while classical models obtained 0.81-0.89. The largest changes (39%) occurred in the years 2022-2023, coinciding with the period of greatest military activity. The proposed method achieved the highest classification quality indices (F1 = 0.93, Acc = 0.98 for 2021), surpassing global products and models based on AlphaEarth. In subsequent years, high stability was maintained (F1 ≥ 0.88), confirming the effectiveness and robustness of the approach under various environmental conditions

This study examines the landscape dynamics in the region surrounding Comandante Ferraz Antarctic Station, Keller Peninsula, King George Island, focusing on the quantification of land cover changes over 23 years. Emphasis is placed on the integration of a multitemporal Landsat time series (2001–2024) within a standardized spatio-temporal data cube framework, coupled with a Random Forest (RF) classification approach. This methodology enables consistent pixel-wise trajectory analysis across seven distinct epochs. The RF models achieved robust performance, with F1-scores for dominant classes like water and soil typically exceeding 0.90, although seasonal snow and ice showed greater spectral ambiguity in transitional months. Quantitative results from the transition matrices reveal a significant landscape reconfiguration: while ice (85.3%) and soil (81.2%) showed high persistence, a prominent trend of deglaciation was identified, characterized by the transition of ice and snow into exposed soil and the emergence of pioneer vegetation communities detected from 2014 onwards. The study demonstrates that the integration of machine learning and data cubes provides a powerful tool for monitoring environmental shifts in high-latitude maritime Antarctica, supporting long-term ecological assessments and climate impact modeling.