GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 2.2 GNGTS 2024 therefore provided in aggregated format (i.e., a number of buildings are located on a point belonging to a constantly-spaced grid). Fig. 2. a) Urban and rural mask provided by the GRUMP dataset, b) example of distribuCon of naConal and sub- naConal scale buildings data on the variable-resoluCon grid (Pibore et al., 2020), c) distribuCon of buildings data on the high-resoluCon Facebook populaCon grid. The building typologies were enriched with age informaCon based on the characterisCcs extracted from past projects and from local partners' data. As for the storey number, similarly for the age of construcCon, a value was associated to each building typology based on the informaCon provided with the EMCA macro typologies (see Wieland et al., 2015 for details) and on the data collected from past projects and/or provided by local partners. Figure 3 (above) shows examples of images of residenCal buildings provided by local partners in the Kyrgyz Republic and Tajikistan (lep and right columns) for typical precast panel buildings (a, b) and adobe buildings (d, e). These images contributed to the characterizaCon of the building types and were used for the elaboraCon of the capacity building acCviCes and especially for the training material. Figure 3 below shows the spaCal distribuCon of one sub- typology of the EMCA1 typology, unreinforced masonry (URM). The map shows the spaCal distribuCon of buildings in the enCre Central Asia region (lep) and for a selected study area (right) with a resoluCon of 500 metres. Similar maps can also be created for other building typologies.