Conference Agenda

In 2024, the Singapore Land Authority (SLA) commissioned Woolpert to conduct a large-scale aerial mapping initiative under the National 3D Mapping Programme to support Smart Nation applications, urban planning, and geospatial analytics. This project, executed between 2024 and 2025, delivered high-resolution imagery and LiDAR datasets across approximately 750 km², covering mainland Singapore and offshore islands. This was the third epoch of 3D mapping in Singapore with previous surveys conducted by Woolpert (then AAM) in 2014 and 2019

Accurate, high-resolution land use and land cover data are critical for effective environmental monitoring, watershed management, and sustainable urban and peri-urban planning within rapidly urbanizing regions such as the Toronto and Region Conservation Authority (TRCA) jurisdiction in Ontario, Canada. TRCA has conventionally relied on manual mapping approaches to delineate its LULC inventory; however, this method is labour-intensive and prone to temporal inconsistencies across updates. To address these challenges, we developed TRCA-AutoMap, a deep learning-based automated mapping framework to generate fine-scale LULC products using 3-m PlanetScope imagery. TRCA-AutoMap integrates two principal modules. The first module is designed to enhance the model’s ability to detect and differentiate objects across spatial scales. By leveraging multi-extent feature encoding and pyramid pooling, the convolutional neural networks capture both fine-texture and contextual information, thereby improving segmentation accuracy and spatial coherence . The second module focuses on optimizing the model’s understanding of varying imaging conditions. It utilizes a group of autoencoders to mitigate radiometric and environmental differences among input images, thereby maintaining the model's reliability across varied lighting conditions, sensor types, and atmospheric conditions. This process enhances the stability of PlanetScope imagery over time and consistency between different scenes. The framework significantly reduces manual processing effort, ensures classification consistency, and supports annual LULC updates. Quantitative and visual evaluations confirm that the model accurately captures fine-scale vegetation heterogeneity and urban expansion dynamics.

With the advancement of urbanization and digital twin city development, urban 3D data are characterized by large volume, heterogeneity, and structural complexity. Traditional spatial data management methods face limitations in hierarchical organization, retrieval efficiency, and redundancy control, and the lack of a unified spatial coding system hinders multi-source data integration. This paper proposes a method for urban 3D data management and representation based on BeiDou grid coding and adaptive voxel modeling. The method converts point cloud data from local coordinates to the 2000 National Geodetic Coordinate System, applies 36-bit 3D BeiDou grid coding, performs adaptive octree voxel partitioning based on point cloud density, elevation variation, and class entropy, and binds spatial, geometric, and semantic attributes at the voxel level. Using the SensatUrban dataset, the method is compared with fixed-resolution voxel modeling, latitude-longitude indexing, and R-tree indexing in terms of voxel quantity, data storage, and retrieval time. Results show that it reduces voxel count by 28.1% and storage volume by 13.6% while maintaining high-precision representation, and the BeiDou grid-based indexing significantly improves query efficiency and stability. The proposed approach balances visualization quality and computational efficiency, providing an effective solution for large-scale urban 3D data management.

Smartphone 3D-scanning apps are becoming popular tools for heritage documentation, but their accuracy and reliability remain unclear. This contribution presents controlled laboratory experiments using several iPhone-based scanning applications, comparing their point clouds to high-precision reference data. The study evaluates geometric accuracy, completeness, and reconstruction geometric stability, highlighting the strengths and limitations of current mobile scanning solutions. Practical recommendations are provided for heritage professionals and field teams, along with future directions for improving smartphone-based documentation using AI-enhanced depth estimation.

The generation of 3D urban morphology models from 2D urban morphology maps has been widely explored. Traditional methods use modelling software, such as Rhino, which lack georeferencing, elevation, and automation. In this study, we developed an open-source Python framework for automatic generation of 3D city blocks, including elevation, from 2D colour-graded building heightmaps and urban morphology input. We utilised the UT-GLOBUS and GlobalBuildingAtlas building datasets to generate heightmaps and retrieved other urban morphology features, such as waterbodies, parks, roads, and trees, from OpenStreetMap to form the input raster patches. The framework generates height and colour maps based on the input features, which are extruded in 3D and exported into multiple standard 3D GIS formats such as CityGML and CityJSON. Six global cities: Sydney, New York, London, Rio de Janeiro, Hong Kong, and Singapore, were modelled to demonstrate the framework’s applicability. Validation includes qualitative comparison with Google Earth 3D data and quantitative comparison against official LiDAR-derived DSMs for four cities. Quantitative results show moderate height errors and good spatial agreement of building footprints, reflecting the expected differences between simplified LoD1 block models and detailed DSM representations. Our framework results show promising potential in the field of 2D to 3D mapping for the creation of 3D city models for urban climate modelling and environmental analysis. The generated 3D models can be downloaded at https://doi.org/10.5281/zenodo.17620303.