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About Us

The Multi-Mission Algorithm and Analysis Platform (MAAP) is a collaborative, open-science platform jointly developed by NASA and the European Space Agency (ESA). It provides a unified environment where data, algorithms, and cloud computing converge. The mission is to make Earth observation research faster, more inclusive, and entirely transparent. By removing the burden of infrastructure management and the "data gravity" of massive file transfers, MAAP allows scientists to focus on what matters: discovery.

Use the full platform, or just the parts you need. Bring your own containerized code to use as a processing backend, or simply use the workspace for interactive exploration. The MAAP platform is available to NASA-funded researchers. To request access, please contact MAAP support. 

Contact ROSES support for proposal-related questions.

What MAAP Provides

  • A cloud-native development environment (JupyterLab) co-located with petabytes of NASA and ESA data for zero-latency access
  • A scalable data processing system with customizable hardware (GPU, memory-optimized, compute-optimized) and cost-saving spot instances
  • An algorithm catalog for containerizing, versioning, and sharing reproducible, peer-verifiable algorithms
  • Dynamic visualization and open APIs for integrating custom tools, dashboards, and external pipelines
  • Real-time performance dashboards and cross-agency collaboration via an ESA-NASA federated layer

Who Should Use MAAP

  • Individual researchers and scientists working on Earth observation at any scale
  • NASA and ESA agency teams requiring shared infrastructure and cross-agency collaboration
  • Data-intensive research teams needing reproducible, scalable workflows without managing infrastructure

Architecture

Efficiency is built into the architecture. MAAP is strategically co-located with the NASA Distributed Active Archive Centers (DAACs) within the Amazon Web Services (AWS) us-west-2 region.

By placing the computing power in the same physical cloud region as the data archives, MAAP provides:

  • Low-latency access: Analyze petabytes of data at backbone speeds
  • Cost efficiency: Eliminate the expensive "egress" fees associated with moving data between regions or out of the cloud
  • High-performance scaling: Process massive datasets instantly without the bottleneck of traditional downloads

Modular and Configurable Components

MAAP is a modular, cloud-native ecosystem designed to accelerate Earth science. Rather than a single "black box" tool, MAAP provides a suite of interconnected services that empower researchers to develop, scale, and share their work without the overhead of managing infrastructure.

Whether you are an individual researcher or part of a global agency team, MAAP adapts to your workflow.

  • Accelerate Development: Use the Algorithm Development Environment (ADE) to jump straight into analysis with JupyterLab-based workspaces supporting multiple programming languages and development tools. Instances come pre-installed with scientific libraries co-located with the data, so you can stop configuring environments and start writing code.
  • Scale Effortlessly: Transition from a prototype to global-scale processing using the Data Processing System (DPS). Built on mission-grade architecture (HySDS), it provides dedicated queues and auto-scaling computing so your algorithms run at scale instantly. 
    • Customized Performance: Move beyond "one-size-fits-all." The MAAP team provisions hardware tailored to your specific workload including GPU instances for machine learning, memory-optimized nodes for large datasets, and compute-optimized instances for CPU-intensive processing.
    • Cost-Optimized Scaling: Maximize your research budget with support for AWS Spot instances, allowing for massive batch workloads at a fraction of the standard cost.
    • Dedicated Resources: With independent queue configurations, your team never has to compete for shared resources, ensuring consistent performance for every run.
  • Ensure Reproducibility: The Algorithm Catalog turns your code into a lasting asset. By containerizing and versioning every algorithm via CI/CD pipelines, MAAP ensures your science is discoverable, reusable, and peer-verifiable.
  • Direct Data Access: Stop downloading and start discovering. Use our STAC and CMR Catalogs to query petabytes of NASA and ESA data directly from your workspace, eliminating the need for local storage or massive data transfers.
  • Dynamic Visualization: Raster, multi-dimensional, and Vector-based dynamic tiling and mosaic solutions on standardized APIs. Add any cataloged data as a layer to web, notebook, and desktop maps. We also include scrolly-telling widgets for adding maps to websites.
  • Build Your Own Ecosystem: Open APIs mean MAAP isn't a silo. Connect your own visualization tools, custom dashboards, or external pipelines directly to our STAC endpoints and programmatic interfaces.
  • Visibility: Real-time metrics are provided via user-facing dashboards for job performance, resource utilization, cost estimates, and debugging that are available on demand, not by special request.

Our Story

MAAP was born from a looming challenge. As next-generation missions like ESA’s BIOMASS, NASA’s GEDI, and the joint NASA-ISRO NISAR mission prepared to launch, it became clear that traditional research methods — downloading files to local machines — would no longer work. The sheer volume and complexity of data required a new paradigm: bringing the researcher to the data.

Timeline

  • The Concept: NASA and ESA teams conceived MAAP as a first-of-its-kind interagency platform governed by open science principles.
  • 2019 Pilot: A successful pilot was launched, focusing on airborne and field campaign data.
  • 2021 Public Launch: MAAP Version 1.0 was released, establishing a workspace where all software and algorithms were open-source and reproducible by design.
  • Today & Beyond: While the platform's roots are in biomass, MAAP now supports a global community working on everything from regional field campaigns to global scale data processing.

Contact Us

If you are interested in using MAAP for an ongoing project, future proposal, or potential collaboration, please email MAAP support or visit the MAAP website.