Reconstrucción de edificios y análisis urbanístico de centros históricos con fotogrametría aérea

[EN] Historical city centers are complex scenarios to be reconstructed in 3D. Advances in automated 3D reconstruction are useful to apply urban analysis that otherwise will require a lot of human effort. In this paper, urban parameters are automatically derived to quantify the urban analysis in historical city centers. Particularly, an aerial photogrammetric flight is used as input data to reconstruct 3D models of buildings with metric capabilities. The results reveal that geometric information of buildings (heights, areas and volumes) and urban density attributes (building coverage ratio and floor area ratio) plays an essential role in the design, planning and management of historical cities. The approach developed was validated in the historical city center of Trento (Italy) using cadastral data and a mobile mapping system (MMS) as ground-truth.[ES] Los centros urbanos históricos son escenarios complejos para su reconstrucción tridimensional. Los avances en la reconstrucción automática son de gran utilidad para realizar análisis urbanísticos que de otra manera requerirían un elevado esfuerzo humano. En este artículo, se derivarán de forma automática parámetros urbanísticos para el análisis de los centros históricos. En particular, se utiliza un vuelo fotogramétrico como base para la obtención de modelos 3D de edificios con propiedades métricas. Los resultados revelan que la información geométrica de los edificios (alturas, áreas y volúmenes) y los atributos de densidad urbana (intensidad de ocupación del suelo en 2D y 3D) juegan un papel esencial en el diseño, planificación y gestión de los centros históricos. El enfoque propuesto fue validado en el centro histórico de la ciudad de Trento (Italia) utilizando datos catastrales y un sistema de cartografiado móvil como referencia geométrica.S

Reconstrucción de edificios y análisis urbanístico de centros históricos con fotogrametría aérea

Historical city centers are complex scenarios to be reconstructed in 3D. Advances in automated 3D reconstruction are useful to apply urban analysis that otherwise will require a lot of human effort. In this paper, urban parameters are automatically derived to quantify the urban analysis in historical city centers. Particularly, an aerial photogrammetric flight is used as input data to reconstruct 3D models of buildings with metric capabilities. The results reveal that geometric information of buildings (heights, areas and volumes) and urban density attributes (building coverage ratio and floor area ratio) plays an essential role in the design, planning and management of historical cities. The approach developed was validated in the historical city center of Trento (Italy) using cadastral data and a mobile mapping system (MMS) as ground-truth.Los centros urbanos históricos son escenarios complejos para su reconstrucción tridimensional. Los avances en la reconstrucción automática son de gran utilidad para realizar análisis urbanísticos que de otra manera requerirían un elevado esfuerzo humano. En este artículo, se derivarán de forma automática parámetros urbanísticos para el análisis de los centros históricos. En particular, se utiliza un vuelo fotogramétrico como base para la obtención de modelos 3D de edificios con propiedades métricas. Los resultados revelan que la información geométrica de los edificios (alturas, áreas y volúmenes) y los atributos de densidad urbana (intensidad de ocupación del suelo en 2D y 3D) juegan un papel esencial en el diseño, planificación y gestión de los centros históricos. El enfoque propuesto fue validado en el centro histórico de la ciudad de Trento (Italia) utilizando datos catastrales y un sistema de cartografiado móvil como referencia geométrica

Multi-target regressor chains with repetitive permutation scheme for characterization of built environments with remote sensing

Multi-task learning techniques allow the beneficial joint estimation of multiple target variables. Here, we propose a novel multi-task regression (MTR) method called ensemble of regressor chains with repetitive permutation scheme. It belongs to the family of problem transformation based MTR methods which foresee the creation of an individual model per target variable. Subsequently, the combination of the separate models allows obtaining an overall prediction. Our method builds upon the concept of so-called ensemble of regressor chains which align single-target models along a flexible permutation, i.e., chain. However, in order to particularly address situations with a small number of target variables, we equip ensemble of regressor chains with a repetitive permutation scheme. Thereby, estimates of the target variables are cascaded to subsequent models as additional features when learning along a chain, whereby one target variable can occupy multiple elements of the chain. We provide experimental evaluation of the method by jointly estimating built-up height and built-up density based on features derived from Sentinel-2 data for the four largest cities in Germany in a comparative setup. We also consider single-target stacking, multi-target stacking, and ensemble of regressor chains without repetitive permutation. Empirical results underline the beneficial performance properties of MTR methods. Our ensemble of regressor chain with repetitive permutation scheme approach achieved most frequently the highest accuracies compared to the other MTR methods, whereby mean improvements across the experiments of 14.5% compared to initial single-target models could be achieved