## 1 Introduction
and rapid dynamics that require high temporal granularity in monitoring. Thus, neither is the potential of EO to fill data gaps in official statistical databases sufficiently explored, nor are the most suitable EO methods and data sources in terms of cost-benefit definition [18]. The recent broad availability of timely satellite images unveils yet a different challenge: there are large amounts of data to be analysed, for which traditional techniques are not enough. In 2012, the \"deep-learning revolution\" opened up an entirely novel frontier. Modern Machine-Learning boosted techniques such as the detection and automatic counting of objects, semantic segmentation, and generic image classification [19]. For example, deep learning has proven to be incredibly versatile [20]. An essential role was played by large benchmark datasets for image analysis, such as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which has served as a testbed for the development of deep learning networks in computer vision. However, deep-learning architectures as such are not sufficiently operational to fill data gaps for NSOs. For example, they are typically set to small images with three VIS \"channels\" (red, green, and blue), stored as image files (e.g., PNG, JPEG) [19]. Thus, these architectures are not set to the use of satellite images, which are often large files, including a geo-reference, with many multispectral channels [20]. Recent research efforts are aimed at designing deep network architectures tailored to specific EO data types. For instance [21], [PERSON] et al. introduced a multiresolution fully convolutional network developed for the analysis of VHR images [18]. The network (dubbed FuseNet/ReuseNet) is able to fuse the panchromatic and multispectral images and perform land-cover classification considering contextual label information in one end-to-end learning framework [21]. Other networks have been specifically designed for the analysis of Synthetic Aperture Radar (SAR) data [22; 23]. While the former CEO of Google, [PERSON], stated that image recognition is a \"solved problem\" [19], this is not true for the EO deep-learning community, with many unsolved challenges for even basic workflows. For example, large archives of (multi-temporal) ground-truth data are missing for deep-learning algorithms, which generally are very training intensive. Unlike regular image recognition, the method to create a ground-truth dataset (whether it is collecting field data or carried out with specialised work of a photo-interpreter) is a decision with all sorts of consequences, such as the quality of the results or the cost of the study itself. During a conference on EO and Statistics organised by the National Institute of Statistics and Geography of Mexico and the University of Twente (recoding available [[https://slummap.net/index.php/2020/08/07/webinar-earth-observations-statistics/](https://slummap.net/index.php/2020/08/07/webinar-earth-observations-statistics/)]([https://slummap.net/index.php/2020/08/07/webinar-earth-observations-statistics/](https://slummap.net/index.php/2020/08/07/webinar-earth-observations-statistics/)) (accessed on 15 January 2021)), it was showcased that EO-based proxies of socio-economic and demographic data can provide timely and relevant information, including extensive spatial coverage as well as multi-temporal data. The aim of this paper is to showcase new avenues to unlock the full potential of EO in support of generating sociodemographic knowledge, focusing in particular on the provision of data on slums. This challenge exhibits three major dimensions where innovation is urgently required: (1) defining a suitable aggregation level of EO data in terms of multi-scale data provision; (2) a critical analysis of data costs/benefits; and (3) the innovation in methods to unlock the full potential of EO. Section 2 of this paper provides an analytical framework that highlights the potential of EO data.