To understand the urban climate, predict the effect of urbanization or attend to improve the impact of some human activities, it is necessary to have a good understanding of the role of the urban surface. Indeed it has been demonstrated that surface forms affect urban microclimate (Givoni 1989, Oke 1981, 1988) and therefore changes the consumer behaviour of residents especially the building energy consumption (Santamouris, 2001, Ohashi et al., 2007). The urban territory is continuously changing: high-rise buildings densification, new road infrastructures, increase of impervious surfaces, consumption of agricultural and natural areas… The result is new border, new shapes and new morphology for the urban geometries. In this context, monitoring urban changes became a challenge for urban planners and decision makers. Geographical Information System (GIS) applications are increasingly being used to compute a set of indicators such as the Sky View Factor, the mean building height, the compactness ratio… All of theses indicators are used to study and monitor the urban structure (Long et al 2003, Bocher et al 2009). Besides, in the late 1990s, a large number of GIS-based tools have been developed by taking advantage of data organisation, spatial analysis and visualisation (eg. cartography). These three functions coincide with the focus of an indicator that needs to organize data, to quantify and to communicate. If this diversity is valuable, on the other hand it can also act as a disincentive for the scientists and urban stakeholders communities. These tools are often build to answer a particular subject (mono-thematic approach). Moreover, most of them are based on proprietary softwares which limits their distribution, the possibility to examine their implementation (algorithm) since the main software is required to run the tool (black box) (Steiniger and Bocher, 2009, Steiniger and Hay, 2009). Last but not least, the definitions used to compute an indicator may differ according to the authors. This situation is in sharp contrast with the needs of the scientific community to share results and experiences, and to experiment with new methods. Moreover, it is inconsistent with number of laws and regulations relating to the protection of the environment that promote common indicators. To fill this gap, we propose a new open source spatial database, called H2GIS (http://www.h2gis.org/), to manipulate and process geographic and alphanumeric data (Gouge et al, 2014). H2GIS is a spatial extension of the Relational Database Management System (RDBMS) H2 Database Engine (http://www.h2database.com/) in the spirit of PostGIS (http://postgis.net/). It adds support for managing spatial features and operations on the new Geometry type of H2. H2GIS is fully compliant with the OGC’s Simple Features for SQL (SFSQL) 1.2.1 standards (Herring 2010, 2011). In this paper we show how the spatial RDBMS H2GIS should be an ideal framework to model the urban data (store and distinguish spatial relationships), create a generic set of spatial urban indicators and used them with massive data (scalable, multi-core processing). As an illustration, H2GIS is used in the MApUCE project which aims to integrate in urban policies and most relevant legal documents quantitative data from urban microclimate, climate and energy. Based on literature review, we offer an open spatial analysis toolbox to study the urban surface.