Multi-Criteria GIS for Sponge City Planning with Open Data Sources in Vigo (Spain)

Sponge cities are renowned for their efficacy against extreme weather events, reducing surface runoff, managing stormwater, and mitigating flood risks. Moreover, they present multifaceted advantages by integrating blue-green infrastructure, enhancing urban sustainability, and improving water quality. The trend of their expansion beyond China marks a significant development in climate-resilient urban planning. This study pioneers the use of open data to locate suitable sites for sponge Low Impact Development (LID) solutions, showcasing Vigo (Spain) as a viable case for mid-sized cities. Input data is obtained from administrative cartography (DTM, hydrogeology, land cover, river courses, and demographic census) and satellite imagery (impervious coverage, vegetation, and surface temperature) from Landsat 8 and MODIS calculating three spectral indices (NISI, NDVI, NDIH). A robust Geographical Information System (GIS) method is proposed weighting the multi-criteria with AHP matrix. Three main potential sites are identified for deploying specific sponge LID strategies, as green roofs, green parking, or rain gardens. Nevertheless, while the method swiftly identifies intervention sites on a municipal scale, conclusive decisions necessitate terrain insights, public sentiments, urban regulations, and funding considerations

Machine and deep learning implementations for heritage building information modelling : a critical review of theoretical and applied research

Research domain and Problem: HBIM modelling from point cloud data has become a crucial research topic in the last decade since it is potentially considered as the central data model paving the way for the digital heritage practice beyond digitization. Reality Capture technologies such as terrestrial laser scanning, drone-mounted LiDAR sensors and photogrammetry enable the reality capture with a sub-millimetre accurate point cloud file that can be used as a reference file for Heritage Building Information Modelling (HBIM). However, HBIM modelling from the point cloud data of heritage buildings is mainly manual, error-prone, and time-consuming. Furthermore, image processing techniques are insufficient for classification and segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Due to the challenges and bottlenecks in the scan-to-HBIM process, which is commonly criticized as complex with its bespoke requirements, semantic segmentation of point clouds is gaining popularity in the literature. Research Aim and Methodology: Therefore, this paper aims to provide a thorough critical review of Machine Learning and Deep Learning methods for point cloud segmentation, classification, and BIM geometry automation for cultural heritage case study applications. Research findings: This paper files the challenges of HBIM practice and the opportunities for semantic point cloud segmentation found across academic literature in the last decade. Beyond definitions and basic occurrence statistics, this paper discusses the success rates and implementation challenges of machine and deep learning classification methods. Research value and contribution: This paper provides a holistic review of point cloud segmentation and its potential for further development and application in the Cultural Heritage sector. The critical analysis provides insight into the current state-of-the-art methods and advises on their suitability for HBIM projects. The review has identified highly original threads of research, which hold the potential to significantly influence practice and further applied research

A review on deep learning techniques for 3D sensed data classification

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches including; RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.Comment: 25 pages, 9 figures. Review pape

Automatic CORINE land cover classification from airborne LIDAR data

Point clouds provide valuable information that is not contained in satellite or aerial images. In this work, the potential of airborne LIDAR data for automatic land cover classification following the CORINE standard is evaluated. The methodology consists on the ordering of the point clouds by means of grid maps and rasterized for their use in the training of a Deep Learning classifier model ResNet-50. Three exclusive features of this type of information are extracted: height difference between points, average intensity and number of returns. The methodology has been tested in one case study at level 1 of CORINE inventory, reaching a 73.5% accuracy and a 59,8% Cohen Kappa coefficient. The main confusion occurs between types with strong similarities