Conference Agenda

Daily satellite-derived nighttime lights (NTL) are increasingly used to monitor electricity disruption and recovery, but their reliability in tropical regions is constrained by persistent cloud cover and intermittent observation. This study adopts a diagnostic-first framework that treats observability as a prerequisite for interpretation, determining when daily NTL signals are sufficiently supported to reflect underlying grid dynamics. Using NASA’s VIIRS Black Marble product, we quantify spatial and temporal completeness and radiance stability across the Samar–Leyte sub-grid in the Philippines. Results show that observations are highly intermittent and spatially heterogeneous, with urban areas providing more stable and interpretable signals, while rural regions remain noise-dominated.

To assess whether reliability-qualified NTL reflects electricity demand, DNB-BRDF radiance is aligned with hourly load data from the National Grid Corporation of the Philippines (NGCP) using settlement-based masks derived from GHSL SMOD. Alignment is evaluated using correlation, error, and retained coverage, combined into a composite score. Strongest agreement occurs under low to mid-range valid-pixel thresholds and within urban-focused masks, which balance signal fidelity and temporal continuity at the cost of reduced coverage. Replication across four additional Visayas sub-grids shows that optimal threshold–mask configurations vary by region, reflecting differences in cloud regime and settlement structure.

These results establish explicit conditions under which daily NTL can be interpreted as a proxy for grid dynamics. The framework provides a reproducible basis for reliability-qualified analysis using globally available datasets and can be tested in other cloud-prone regions where ground-based data are limited.

The maintenance of airport pavements is critical to ensuring the safety and efficiency of air operations. Conventional inspection methods are often time-consuming, subjective, and prone to inconsistencies in data collection. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a potential alternative for improving inspection efficiency and measurement accuracy.

This study evaluates the applicability of UAV-based photogrammetry for the detection and quantification of pavement distresses under conditions representative of airport infrastructure. Image data were acquired at different flight altitudes and overlap configurations and processed using Structure-from-Motion techniques to generate high-resolution orthomosaics and Digital Elevation Models (DEMs). The resulting datasets were analyzed to identify, delineate, and classify deterioration types and severity levels.

The results indicate that a flight altitude of 10 m combined with 80% longitudinal and 70% transversal overlap provides an optimal balance between spatial resolution and operational efficiency. Under unobstructed conditions, photogrammetric analysis detected more than 98% of existing distresses and enabled more precise geometric delineation compared to traditional field-based methods. Undetected distresses were primarily associated with shadowed or obstructed areas, highlighting the influence of environmental conditions on detection performance.

Overall, the findings demonstrate that UAV-based photogrammetry is a reliable and efficient approach for pavement condition assessment, with significant potential to enhance data quality and reduce inspection time in airport infrastructure management.

3D change detection is essential for monitoring infrastructure, environmental dynamics, and natural hazards. However, existing algorithms are often evaluated on single-scene datasets, and their generalization across varied real-world scenes remains largely unexplored due to the absence of a universal benchmark. To address this issue, we propose MultiChange3D, a multi-scene, multi-sensor 3D change detection dataset for identifying geometric changes in 3D space. The dataset provides registered pairs of point clouds with ground-truth geometric change labels, enabling standardized evaluation across different methods. To demonstrate the use of the MultiChange3D dataset, we benchmark an initial set of approaches on a subset of the dataset. The evaluated methods include classical Euclidean distance-based methods (C2C, M3C2), 3D displacement estimation-based approaches (F2S3, Landslide-3D), and deep learning-based classification methods (KPConv, EF-KPConv, PGN3DCD). Quantitative and qualitative analyses indicate the strengths and limitations of the evaluated methods, highlighting the challenges in cross-scene generalization under variations in point density, scale, and types of changes. The full dataset and evaluation code is openly available at: https://github.com/3DOM-FBK/multichange3d.

Quantifying topographic dynamics from 3D point cloud time series is essential for geoscientific applications. However, laser scanning data typically varies between epochs in point density due to differing survey properties. These irregularities present a challenge for change detection, particularly across multi-temporal and multi-modal data. We propose a new approach, adaptive temporal aggregation, as an extension of the Multiscale Model to Model Cloud Comparison (M3C2) algorithm. Driven by a local point density requirement, our method employs both a spatial and a temporal neighborhood. If a core point's neighborhood is too sparse for M3C2 estimation, an iterative temporal search progressively incorporates data from temporally adjacent epochs until the density requirement is met or a maximum temporal window is reached. This adaptive process ensures sufficient local density while preventing unnecessary temporal aggregation, a key advantage over global aggregation. We evaluated our method on a multi-modal dataset from the Mont-Saint-Michel Bay, France (38 irregular epochs, ~1 decade). Results demonstrate significantly improved change detection, increasing completeness by >13% (vs. standard M3C2) and accuracy by 31% (vs. fixed-window averaging). Our work provides a robust approach for enhancing 3D change detection algorithms for complex, real-world 4D datasets, enabling higher accuracy and completeness in analysing surface dynamics.