Map land cover use and building footprints at national scales.
Deliver scalable raster and vector maps online.
National maps of building footprints and croplands are crucial for many projects, such as agricultural intensification, vaccine delivery, and disaster relief. These maps allow field surveyors and front-line workers to optimize their logistics. They also allow policy makers and entrepreneurs to better quantify the needs of a country and allocate resources accordingly.
Throughout the Global South, governments and businesses struggle to generate these maps. Running in-person surveys with large ground teams is extremely time-consuming and expensive.
To address these needs, QED (https://qed.ai) has built automated methods for mapping buildings and croplands across nations. By coupling deep learning with crowdsourced collection of precise training data, we can well-approximate mapping produced by traditional manual methods, while cutting the time and cost by many orders of magnitude.
We generate maps for both public and private sector clientele in specified areas of interest. Maps are rendered through the web in a scalable-fashion, supporting both raster and vector formats at multiple zoom levels. Following the slogan that “Seeing is Believing”, our maps can be inspected for their veracity, resulting in information that is often more up-to-date or reliable than government statistics. Below are several examples.
Nigeria has the largest population and economy in Sub-Saharan Africa (SSA). With the recent plunge in the price of oil, Nigeria is urgently seeking strategies to diversify its economy. Strong attention is being placed toward Nigeria’s agricultural sector. Better availability of reliable and up-to-date agricultural data will help both the government and private industry to inform policy and investment in the agricultural sector. And basic statistics on the hectarage of cropland and land area by major crop type are outdated, if they exist at all. Below is an example of cropland mapping, generated by QED across Nigeria, to fill this gap. Please use the interactive slider to compare satellite imagery against our classifications.
Effective management of farmlands, grazing reserves, forests, irrigation, wildlife habitats, and endangered species requires the classification of land cover at national scales. Using machine learning, we are able to scalably generate maps such as the one below, differentiating between croplands, urbanization, forests, shrublands, and rivers and estuaries. Please use the interactive slider to compare the satellite imagery with our classifications.
Many countries in Sub-Saharan Africa can benefit from greater access to electricity. Mapping of building footprints at national-scale, particularly in rural areas, can assist companies with planning electrical layout and utility placement. Below are some examples from Malawi, which we have mapped in its entirety.
Updated soil information is extremely scarce throughout the world, despite the fact that soil is one of the most precious natural resources of any nation. This information is acquired at great economic and temporal expense, requiring both field work and analytical chemistry. Through the AfSIS project (2009-2019), we have expertise in pairing soil data with machine learning and remote sensing to make digital soil maps.