Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop’s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.

Multi-temporal analysis of census small-area microdata is hampered by the fact that census tract shapes do not often coincide between census exercises. Dasymetric mapping techniques provide a workaround that is nonetheless highly dependent on the quality of ancillary data. The objectives of this work are to: (1) Compare the use of three spatial techniques for the estimation of population according to census tracts: Areal interpolation and dasymetric mapping using control data—building block area (2D) and volume (3D); (2) demonstrate the potential of unmanned aerial vehicle (UAV) technology for the acquisition of control data; (3) perform a sensitivity analysis using Monte Carlo simulations showing the effect of changes in building block volume (3D information) in population estimates. The control data were extracted by a (semi)-automatic solution—3DEBP (3D extraction building parameters) developed using free open source software (FOSS) tools. The results highlight the relevance of 3D for the dasymetric mapping exercise, especially if the variations in height between building blocks are significant. Using low-cost UAV backed systems with a FOSS-only computing framework also proved to be a competent solution with a large scope of potential applications.

Presently, the use of new technologies for the acquisition of 3D geographical data on time is very important for urban planning. Applications include evaluation and monitoring of urban parameters (ie. volumetric data),indicators of an urban plan, or monitoring built-up areas and illegal buildings. This type of 3D data can be acquired through an Airborne Laser Scanning system, also known as LiDAR (Light Detection And Ranging) or by Unmanned Aerial Vehicles (UAV). The aim of this article is to use and compare these two technologies for extracting building parameters (facade height and volume). Existing literature evaluates each technology separately. This work pioneers benchmarking between LiDAR and UAV point-clouds. The basic function of LiDAR is collecting a georeferenced and dense 3D point-cloud from a laser scanner during flight. Therefore it is possible to obtain a similar 3D point-cloud using processing algorithms for stereo aerial images, obtained by large or small-format digital cameras (the small-format camera implemented in Unmanned Aerial Vehicles). The chosen study area is located in Praia de Faro, an open sandy beach in Algarve (Southern Portugal), limited west by the Ria Formosa barrier island system. The area defined has an extension of 300100m. The methodology is divided in two distinct stages: (1) building parameters extraction, (2) comparative technology analysis. Lidar point-cloud resolution is approximately 6 pts/m2 and UAV point-cloud 60 pts/m2. FOSS technologies have proven to be the most adequate adequate platform for the development and diffusion of advanced analytical tools in the Geographical Information Sciences (GISci). Data management in this paper is supported by a Geographical Database Management System (GDBMS), implemented using PostgreSQL and PostGIS. Statistical analysis is performed using R whilst advanced spatial functions are used in GRASS.

New Developments in Geographical Information Technology for Urban and Spatial Planning: 10.4018/978-1-4666-9845-1.ch094: Measuring urban form, modelling 3D point clouds and visualizing data within an (augmented) mixed reality environment through mobile devices are three of the

New Developments in Geographical Information Technology for Urban and Spatial Planning: 10.4018/978-1-4666-4349-9.ch010: Measuring urban form, modelling 3D point clouds and visualizing data within an (augmented) mixed reality environment through mobile devices are three of the

Three-dimensional urban morphology has gained renewed analytical relevance as advances in remote sensing, photogrammetry, and geospatial data processing have made it possible to derive 3D data and high-resolution digital surface and elevation models. Block-scale morphological analysis remains predominantly two-dimensional, leaving volumetric scale, vertical heterogeneity, and spatial aperture undetectable, while existing 3D approaches treat building metrics as isolated variables or rely on costly semantic city models. This study proposes a parsimonious geometric vector framework in which each urban block is represented as a vector of three conceptually distinct dimensions: Mean Building Volume (MBV, volumetric scale), Height Variation Coefficient (HVC, vertical heterogeneity), and Mean Sky View Factor (SVF, spatial aperture). The framework was operationalized through reproducible GIS and Python 3.12 workflows using a large-scale topographic dataset and a LiDAR-derived Digital Surface Model, and it was tested through K-means clustering of 816 statistical blocks in the municipality of Amadora, Portugal. The results confirm the near-orthogonality of the three dimensions (correlations |r| < 0.25; Variance Inflation Factors ≤ 1.08) and identify five structurally distinct typologies ordered along a volumetric gradient (MBV), in which HVC is the principal differentiating dimension among Types 1–4, and SVF distinguishes Type 5; the partition remains stable under 500-iteration consensus clustering (Adjusted Rand Index = 0.9445). The contribution of this study lies in the transferability of the analytical pipeline rather than of the typology itself, which is specific to Amadora and would require independent derivation elsewhere. The framework provides urban planners with a parsimonious, geometrically transparent, and operationally feasible tool for classifying urban blocks in 3D, complementing 2D analytical approaches and establishing a morphological basis for future climate-responsive analyses.