Visualising accessibility: an interactive tool and two applications to empirical case studies of urban development and public engagement

While a substantial body of literature exists on the theoretical definitions and measures of accessibility, the extent to which such measures are applied into practice to assess project alternatives is less frequent (Geurs and Van Wee, 2004). Recent studies affirm that one of the main barriers to the usability of accessibility measure is the lack of visualisation quality and mapping tool for accessibility representation (te Brömmelstroet et al, 2014), while visualisation tools are commonly recognised as the most effective methodology to facilitate knowledge sharing, particularly in those processes involving public stakeholders and non-experts with different expertise. Starting form this consideration, this paper presents an application of the Interactive Visualisation Tool, named InViTo (Pensa and Masala, 2014a; 2014b; Pensa, et al., 2014; Pensa, Masala and Lami, 2013; Pensa, Masala and Marina, 2013) able to generate maps of the level of perceived accessibility (i.e. “desirability”) of different urban areas. Desirability is here computed as the perceived level of access to different urban items as transport supply (metro and rail stations, public transport stops, parking) and urban activities such as hospitals, schools, museums. In this respect, the concept of desirability encompasses a measure of accessibility to several urban facilities, and the perception that residents of the study area have of such facilities. The InViTo tool allows to build up maps of desirability interactively, by making selection of the chosen items and by giving differential weights to each items. This makes the tools powerful and very useful particularly when discussing and showing analysis results to stakeholders, who could have the opportunity to see in real time the results of different scenario alternatives and assumptions. In the paper two applications are presented. The first one to the empirical case study of Rome, presents the steps to undertake in order to apply the tool: from data gathering, maps coding, and results representation. The second application aims at exploring the potential usability of the tool in engaging public stakeholders into the assessment of different urban development options. Furthermore, the results of a workshop held in Turin, in which public and private stakeholders were interactively involved, are discussed. The paper is organised as follows. In section 2, an overview of the InViTo tool is given with a focus on its applicability formeasuring accessibility. In section 3 the results of the two InViTo applications are discussed. Conclusions are drawn in section 4, with an outlook to undergoing research issues

M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion

Modern Earth Observation systems provide sensing data at different temporal and spatial resolutions. Among optical sensors, today the Sentinel-2 program supplies high-resolution temporal (every 5 days) and high spatial resolution (10m) images that can be useful to monitor land cover dynamics. On the other hand, Very High Spatial Resolution images (VHSR) are still an essential tool to figure out land cover mapping characterized by fine spatial patterns. Understand how to efficiently leverage these complementary sources of information together to deal with land cover mapping is still challenging. With the aim to tackle land cover mapping through the fusion of multi-temporal High Spatial Resolution and Very High Spatial Resolution satellite images, we propose an End-to-End Deep Learning framework, named M3Fusion, able to leverage simultaneously the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR information. Experiments carried out on the Reunion Island study area asses the quality of our proposal considering both quantitative and qualitative aspects