The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys.

The aim of the current article is to think over economic development and urban infrastructure propositions by Salvador 360 Strategic Plan from 2017, which was elaborated by the municipal government. The plan allows analyzing the neoliberal city production approach which, despite its modernizing profile, is just an update of old models that have great potential to worsen structural socio-spatial segregation issues. Neoliberalism, urban spoliation, land value and gentrification are key concepts in this article. Neil Brenner, Nick Theodore, Jamie Peck, David Harvey, Lúcio Kowarick, Carlos Vainer and Inaiá de Carvalho are its main theoretical references. The methodology addresses documental analysis, as well as the analysis of secondary data from official sources (DIEESE and IBGE) and digital platforms analysis (AirBnB website). Based on the results, Salvador 360 Strategic Plan deepens eviction processes experienced by the poor population in the city’s central zones and increases the appropriation of private urban added value and service provision concentration, within a political setback context. This process can aggravate urban environment precariousness and quality of life in the city.

Este artigo apresenta o estado da arte de uma plataforma de monitoramento de áreas verdes urbanas − UrbVerde − para municípios do Estado de São Paulo. Sua construção se baseia em Cloud Computing e sensoriamento remoto orbital. Objetiva-se prover métricas de monitoramento das desigualdades socioambientais a partir das escalas municipal e intraurbana, incluindo indicadores de vegetação, temperatura de superfície e sistemas de espaços livres (parques e praças), relacionando-os com dados populacionais agrupados por setores censitários. Constitui-se, assim, uma base de informações acompanháveis e de livre acesso com potencial de aplicação para a construção de políticas públicas, a exemplo do programa Município VerdeAzul. Por meio das ferramentas interativas de análise e correlação de dados, destaca-se o aspecto inter-relacional das variáveis geográficas envolvidas, representando um ganho de inteligência de governança. Além disso, a abordagem utilizada fornece novos olhares sobre a distribuição e a acessibilidade das infraestruturas verdes nos municípios, promovendo a sensibilização dos gestores e uma maior inclusão da população nos processos de tomada de decisão. Deste modo, espera-se, com o apoio de uma tecnologia disruptiva, estimular conjuntamente o processo de suporte à decisão para a retomada verde na Década da Restauração de Ecossistemas (2021-2030), viabilizando o processo de transformação dos municípios em cidades inteligentes e sustentáveis.

This paper proposes a modification of the Modified Normalized Difference Water Index (MNDWI) called Rescaled Water Index (RWI), aiming to enhance the delineation and performance of water body mapping in urban contexts. To evaluate the proposed index, we compare its performance in mapping Water Surfaces (WSs) in different cities in South America with the results obtained by other well-known water indices, namely, Normalized Difference Water Index (NDWI), MNDWI, Automated Water Extraction Index (AWEIsh, and AWEInsh)). We utilized images from the Sentinel satellites (2A and 2B), all acquired in June 2021, focusing on cities located south of the Tropic of Capricorn to obtain images with the highest incidence of shading, whose spectral response complicates the mapping of WSs. In this scenario, the present study revealed that selecting the best single index for mapping all WSs is an arduous task, as their performances varied across the analyzed locations. Overall, the performance of the indices, evaluated by the partial Receiver Operating Characteristic (pROC) curve and non-water misclassification point restrictions, revealed quite close results, especially considering the RWI, NDWI, and MNDWI. In the six cities analyzed, the RWI outperformed in three. Additionally, when considering the results for all the cities, the RWI outperformed all the other indices. Our study also rainforced the importance of the complementarity of the indices to improve the performance of the mappings. Such results, therefore, were interpreted as an essential contribution to water body mapping, considering its practical applications in environmental monitoring and water resource management in urbanized areas.

Since the approval of the Brazilian Federal Technical Assistance Law, whose objective is to guarantee adequate technical assistance to the residents of poor areas to improve their homes, several municipalities have implemented programs, not always successful, to improve housing in slums. Aiming to subsidize these programs implementation workflow, the present paper analyses use of drones as a tool to gather information about the physical characteristics slum households in Salvador de Bahia city, Brazil. As an experiment we flight over a single census sector within the Alto das Pombas slum, and after image processing we extracted and organized the collected data, extracting the possible information that could be applied to identify and quantify the most precarious houses that could be prioritized from a health improvement perspective. We conclude that many of the necessary data needed, on the urbanistic scale in Brazilian slums, can benefit from drone photogrammetry at low cost and fast execution.