8/5/2023 0 Comments Imap world explorer![]() In this paper, we introduce a new NRT LULC dataset produced using a deep-learning modeling approach. Simultaneous advances in large-scale cloud computing and machine learning algorithms in high-performance open source software frameworks (e.g., TensorFlow 16) as well as increased access to satellite image collections through platforms such as Google Earth Engine 17 have opened new opportunities to create global LULC datasets at higher spatial resolutions and greater temporal cadence than ever before. Nonetheless, globally consistent, near real-time (NRT) mapping of LULC remains an ongoing challenge due to the tremendous computational and data storage requirements. A noteworthy exception is the recent iMap 1.0 15 series of products available globally at a seasonal cadence with a 30 m resolution. Thus, there is a critical need for spatially explicit, moderate resolution (10–30 m/pixel) LULC products that are updated with greater temporal frequency.Ĭurrently, almost all moderate resolution LULC products are available with only limited spatial and/or temporal coverage (e.g., USGS NLCD 10 and LCMAP 11) or via proprietary and/or closed products (e.g., BaseVue 12, GlobeLand30 13, GlobeLand10 14) that are generally not available to support monitoring, forecasting, and decision making in the public sphere. Inability to resolve these processes introduces significant errors in our understanding of ecological dynamics and carbon budgets. While widely used, many important LULC change processes are difficult or impossible to observe at a spatial resolution greater than 100 m and annual temporal resolution 9, such as emerging settlements and small-scale agriculture (prevalent in the developing world) and early stages of deforestation and wetland/grassland conversion. These maps include the National Aeronautics and Space Administration (NASA) MCD12Q1 500 m resolution dataset 4, 5 (2001–2018), the European Space Agency (ESA) Climate Change Initiative (CCI) 300 m dataset 6 (1992–2018), and Copernicus Global Land Service (CGLS) Land Cover 100 m dataset 7, 8 (2015–2019). Annual maps of global LULC have been developed by many groups. Regularly updated global land use land cover (LULC) datasets provide the basis for understanding the status, trends, and pressures of human activity on carbon cycles, biodiversity, and other natural and anthropogenic processes 1, 2, 3. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines. ![]() Furthermore, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. ![]() We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. ![]() Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release.
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