Assessing sustainable urban development trends in a dynamic tourist coastal area using 3D Spatial Indicators

In coastal areas, the tourism sector contributes to the local economy, generating income, employment, investments and tax revenues but the rapid urban expansion creates great pressure on local resources and infrastructures, with negative repercussions on the residents’ quality of life, but also compromising the visitor’s experience. These areas face problems such as the formation of meteorological effects known as heat islands, due to the soil sealing, and increased energy demand in the peak season. To evaluate the impact of urban growth spatial pattern and change, three strategic sustainable challenges—urban form, urban energy, and urban outdoor comfort—were selected. The progress towards sustainability was measured and analyzed in a tourist city in the Algarve region, Portugal, for the period 2007–2018, using geographic information. A set of 2D and 3D indicators was derived for the building and block scales. Then, a change assessment based on cluster analysis was performed, and three different trends of sustainable development were identified and mapped. Results allow detecting the urban growth patterns that lead to more sustainable urban areas. The study revealed that a high sustainable development was observed in 12% of the changed blocks in the study area. All indicators suggest that the growth pattern of the coastal area is in line with the studied sustainability dimensions. However, most of the blocks that changed between 2007 and 2018 (82%) followed a low sustainable development. These blocks had the lowest variation in the built volume and density, and consequently the lowest variations in the roof areas with good solar exposition. The urban development also privileged more detached and less compact buildings. This analysis will support the integration of 2D and 3D information into the planning process, assisting smart cities to comply with the sustainable development goals.info:eu-repo/semantics/publishedVersio

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dimensional urban densification. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both the dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images.Comment: Accepted manuscript including appendix (supplementary file

Selection of Unlabeled Source Domains for Domain Adaptation in Remote Sensing

In the context of supervised learning techniques, it can be desirable to utilize existing prior knowledge from a source domain to estimate a target variable in a target domain by exploiting the concept of domain adaptation. This is done to alleviate the costly compilation of prior knowledge, i.e., training data. Here, our goal is to select a single source domain for domain adaptation from multiple potentially helpful but unlabeled source domains. The training data is solely obtained for a source domain if it was identified as being relevant for estimating the target variable in the corresponding target domain by a selection mechanism. From a methodological point of view, we propose unsupervised source selection by voting from (an ensemble of) similarity metrics that follow aligned marginal distributions regarding image features of source and target domains. Thereby, we also propose an unsupervised pruning heuristic to solely include robust similarity metrics in an ensemble voting scheme. We provide an evaluation of the methods by learning models from training data sets created with Level-of-Detail-1 building models and regress built-up density and height on Sentinel-2 satellite imagery. To evaluate the domain adaptation capability, we learn and apply models interchangeably for the four largest cities in Germany. Experimental results underline the capability of the methods to obtain more frequently higher accuracy levels with an improvement of up to almost 10 percentage points regarding the most robust selection mechanisms compared to random source-target domain selections