New Building Detection API Sentinel-1

AWS Data Exchange/New Building Detection API Sentinel-1 is a Satellite & Geospatial data product listed on AWS Data Exchange and indexed by The Alternative Data Encyclopedia.

AWS Data Exchange publishes New Building Detection API Sentinel-1 as an AWS Data Exchange offering in the Satellite & Geospatial signal family.

The data

API for detecting new buildings from satellite images. Useful for tracking urban development, construction progress, and analyzing population dynamics.

It is one of 52 listings in the Satellite & Geospatial family; between them the practical differences come down to coverage, history depth and how the raw signal is cleaned and delivered.

Structure, access and licensing

Access is through a dataset that is pulled through an AWS Data Exchange product. The listing does not state an explicit refresh schedule, which is worth confirming before backtesting.

The signal

Processed satellite and aerial imagery converted into counts and measurements of physical economic objects: vehicles in parking lots, ships at ports, volumes in storage tanks, crop condition and construction activity. The signal turns remote sensing into site-level time series that estimate stocks and activity before official statistics appear.

Physical observation leads official data: counting cars at retailers approximates same-store sales weeks before earnings, tank measurements inform crude inventory trades ahead of government releases, and crop indices drive yield estimates during growing seasons. SAR extends the edge to night, cloud and denied areas, which is why insurers buy observed flood extents rather than modeled probabilities and commodity desks track dark-fleet activity. Funds pay because the same imagery revisits any asset class location on Earth on a known schedule. Output is georeferenced time series of counted or measured quantities per site, with confidence scores, imagery provenance and observation dates attached to each observation. Product families include analytic feeds that detect objects automatically and derived variables such as soil moisture or vegetation indices. Radar (SAR) products add all-weather, day-night measurement of flooding, ground deformation and vessel presence.

Vendors either task constellations directly or license capacity from operators, then run computer-vision pipelines that segment scenes and count or measure target objects per site. Optical providers operate near-daily global scanning constellations plus sub-daily, high-resolution tasking satellites; SAR operators advertise the world's largest commercial radar fleets with resolutions down to roughly twenty-five centimeters. Counts are calibrated against ground truth where accessible, and delivery layers ship both imagery archives and analysis-ready derived series through APIs.

Caveats and compliance

Cloud cover and revisit cadence create irregular sampling that complicates time-series comparison. Small samples of monitored sites rarely generalize to corporate revenue without weighting assumptions. Algorithm upgrades change object definitions over time, so backtests need frozen model versions, and calibration drift shows up as level shifts rather than noise.

Imagery licenses typically restrict redistribution and resale of native-resolution pixels, while derived counts inherit lighter but real restrictions. Some jurisdictions regulate collection near critical infrastructure, and export-control rules apply to high-capability sensing systems.

Who uses this signal

Commodities, macro and consumer funds convert imagery into inventory, activity and yield estimates ahead of official numbers. Insurers and governments buy the same observations for catastrophe response, and industrials monitor assets and supply chains.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading