This study focused on implementation of the Sustainable Development Goal (SDG) 11 indicators, at local scale, useful in monitoring urban social resilience. For this purpose, the study focused on updating the distribution map of the migrant population regularly residing in Bari and a neighboring town in Southern Italy. The area is exposed to increasing migration fluxes. The method implemented was based on the integration of Sentinel-2 imagery and updated census information dated 1 January 2019. The study explored a vector-based variant of the dasymetric mapping approach previously used by the Joint Research Center (JRC) within the Data for Integration initiative (D4I). The dasymetric variant implemented can disaggregate data from census areas into a uniform spatial grid by preserving the information complexity of each output grid cell and ensure lower computational costs. The spatial distribution map of regular migrant population obtained, along with other updated ancillary data, were used to quantify, at local level, SDG 11 indicators. In particular, the map of regular migrant population living in inadequate housing (SDG 11.1.1) and the ratio of land consumption rate to regular migrant population growth rate (SDG 11.3.1) were implemented as specific categories of SDG 11 in 2018. At the local level, the regular migrant population density map and the SDG 11 indicator values were provided for each 100 × 100 m cell of an output grid. Obtained for 2018, the spatial distribution map revealed in Bari a high increase of regular migrant population in the same two zones of the city already evidenced in 2011. These zones are located in central parts of the city characterized by urban decay and abandoned buildings. In all remaining city zones, only a slight generalized increase was evidenced. Thus, these findings stress the need for adequate policies to reduce the ongoing process of residential urban segregation. The total of disaggregated values of migrant population evidenced an increase of 44.5% in regular migrant population. The indicators obtained could support urban planners and decision makers not only in the increasing migration pressure management, but also in the local level monitoring of Agenda 2030 progress related to SDG 11.

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This paper analyzes two pixel-based classification approaches to support the analysis of land cover transformations based on multitemporal LANDSAT sensor data covering a time space of about 24 years. The research activity presented in this paper was carried out using Lama San Giorgio (Bari, Italy) catchment area as a study case, being this area prone to flooding as proved by its geological and hydrological characteristics and by the significant number of floods occurred in the past. Land cover classes were defined in accordance with on the CN method with the aim of characterizing land use based on attitude to generate runoff. Two different classifiers, i.e. Maximum Likelihood Classifier (MLC) and Java Neural Network Simulator (JavaNNS) models, were compared. The Artificial Neural Networks (ANN) approach was found to be the most reliable and efficient when lacking ground reference data and a priori knowledge on input data distribution.

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