The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

One of the challenges in the fight against poverty is the precise localization and assessment of vulnerable communities’ sprawl. The characterization of vulnerability is traditionally accomplished using nationwide census exercises, a burdensome process that requires field visits by trained personnel. Unfortunately, most countrywide censuses exercises are conducted only sporadically, making it difficult to track the short-term effect of policies to reduce poverty. This paper introduces a definition of vulnerability following UN-Habitat criteria, assesses different CNN machine learning architectures, and establishes a mapping between satellite images and survey data. Starting with the information corresponding to the 2,178,508 residential blocks recorded in the 2010 Mexican census and multispectral Landsat-7 images, multiple CNN architectures are explored. The best performance is obtained with EfficientNet-B3 achieving an area under the ROC and Precision-Recall curves of 0.9421 and 0.9457, respectively. This article shows that publicly available information, in the form of census data and satellite images, along with standard CNN architectures, may be employed as a stepping stone for the countrywide characterization of vulnerability at the residential block level.

Understanding human settlements' geographic location and extent can support decision-making in resource distribution, urban growth policies, and natural resource protection. This research presents an approach to assess human settlement sprawl using labeled multispectral satellite image patches and Convolutional Neural Networks (CNN). By training deep learning classifiers with a dataset of 5,359,442 records consisting of satellite images and census data from 2010, we evaluate sprawl for settlements across the country. The study focuses on major cities in Mexico, comparing ground truth results for 2015 and 2020. EfficientNet-B7 achieved the best performance with a ROC AUC of 0.970 and a PR AUC of 0.972 among various CNN architectures evaluated. To evaluate human settlement sprawl, we introduce an information-based metric that offers advantages over entropy-based alternatives.

This paper explores the strategies employed to develop a system leveraging deep learning and natural language processing techniques for automating aspects of record coding in statistical data products. The work focuses on the Mexican Statistical Institute (INEGI), where the proposed AI model aims to reduce the volume of records requiring manual coding. The experiments conducted demonstrate the potential for these methodological innovations to partially replace traditional manual coding processes. Specifically, a novel phase has been introduced into INEGI’s data encoding workflows, utilizing an AI model to select a subset of records for which the model has a high degree of confidence in assigning the correct codes. This approach seeks to minimize the need for manual intervention. To evaluate the effectiveness of this proposal, a production line was implemented that mirrored the existing process but included the AI-based phase. The results show that the use of deep learning algorithms can achieve a significant 50% reduction in manual coding tasks without compromising the quality of the output.