Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learningbased models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO) architectures were investigated. These networks were adapted, trained and validated on a data set including 87k vehicles, annotated using an interactive semi-automatic tool developed by the authors. Experimental results show that both segmentation and detection models could achieve a precision rate higher than 85 % with a recall rate also high (76.4 % and 71.9 % for Tiramisu and YOLO respectively).

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Nowcasting macroeconomic aggregates have proved extremely useful for policy-makers or financial investors, in order to get real-time, reliable information to monitor a given economy or sector. Recently, we have witnessed the arrival of new large databases of alternative data, stemming from the Internet, social media, satellites, fixed sensors, or texts. By correctly accounting for those data, especially by using appropriate statistical and econometric approaches, the empirical literature has shown evidence of some gain in nowcasting ability. In this chapter, we propose to review recent advances of the literature on the topic, and we put forward innovative alternative indicators to monitor the Chinese and US economies.

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