The MAAP platform is engineered to combine data, algorithms, and computational abilities to process and share Earth Science data at-scale in the cloud. It is employed by science teams using data from NASA’s GEDI (Global Ecosystem Dynamics Investigation), NASA’s ICESat-2, ESA’s BIOMASS, NASA/ISRO’s NISAR mission, and more.
Latest News
ESIP 2024
We will be at ESIP in July 2024! Come say hi. The MAAP Team has organized a session on Cross-Platform Interoperability for Scalable Computing in Open Science Analysis and Algorithm Development. Thursday July 25, 2024…
Towards global spaceborne lidar biomass: Developing and applying boreal forest biomass models for ICESat-2 laser altimetry data
Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA’s ICESat-2 mission provide the first comprehensive look at canopy…
MAAP version 4.0.0 Release
MAAP v4.0.0 has been released. It is a major new release with some breaking changes. Some highlights include: The “Basic Stable” workspace stack has been renamed to “Python (default)”. The associated default “vanilla” conda environment…
Large-Scale Canopy Height Mapping Using C-band InSAR Coherence and GEDI LiDAR Data
Dry forests and savannas have highly heterogeneous horizontal and vertical structure of woody vegetation and high temporal variability in moisture and phenology. This presents distinct challenges to SAR-based approaches to mapping canopy-height compared to tropical…
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Advancing Data Fusion and Research Collaboration for Global Science Initiatives
Sujen Shah will present a poster highlighting several science applications of the MAAP Platform at AGU 2023. In this presentation, we will delve into the remarkable ways in which various teams have embraced and integrated…
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Committee of Earth Observation Satellites (CEOS) Biomass Harmonization
Biomass Harmonization is a coordinated effort of scientists to release forest-carbon estimates obtained with space data in a comparable manner. This helps identify underlying assumptions, definitions and uncertainty-estimation frameworks (Ref 1). By collaborating with scientists…
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