The NASA Landsat Data Collection (NLDC) is a compilation of Landsat multispectral scanner (MSS) scenes and Landsat thematic mapper (TM) scenes. This compilation of scenes represents data collections from four distinct projects including: (1) the Global Change Landsat Data Collection (GCLDC);(2) the Humid Tropical Forest Project (HTFP) collection of source scenes and products; (3) a collection of data from the Committee on Environment and Natural Resources Research [formerly the Committee on Earth and Environmental Sciences (CEES)] that is historically referred to as the CEES collection; and (4) ongoing Landsat data purchases by NASA-funded investigators, starting with the 1996 fiscal year. The NLDC scenes have been screened for cloud cover and band quality resulting in a high grade,high quality data compilation. The GCLDC collection contains Landsat TM scenes that were purchased by NASA from Space Imaging, formerly the Earth Observation Satellite Company,under a special agreement to promote the use of shared data in global change research. The HTFP, the largest component of NASA's Landsat Pathfinder Program, contains Landsat MSS and TM scenes collected over the past 20 years. The goal of the HTFP is to globally map deforestation in the humid tropical forests. The CEES collection is the result of an effort to coordinate data needs among several Federal agencies (e.g.,Environmental Protection Agency, Department of the Interior agencies, National Oceanic and Atmospheric Administration, Department of Defense). These Landsat TM scenes were collected for a variety of research projects. Ongoing NASA purchases of Landsat TM data support NASA scientists and their affiliated researchers in programs and projects including the NASA Research and Analysis Program; the Global Land Cover Test Sites Project; the HTFP, the International Biosphere-Geosphere Programme, the NASA Applications Program; and the Landsat-7 Science Team.

The goal of this project is to develop capabilities for an integrated petabyte-scale Earth science product development, production and collaborative analysis environment. We will deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research. This system will significantly enhance the ability of the scientific community to accelerate transformation of Earth science observational data from NASA missions, model outputs and other sources into science data products and facilitate collaborative analysis of the results. We propose to develop a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale data processing pipelines, perform data visualization, provenance tracking, analysis and QA of both the production process and the data products, and enable sharing results with the community. In terms of the NRA, the project is proposed under 'Data-Centric Technologies' category. The HPC component will interface with the NASA Earth Exchange (NEX), a collaboration platform for the Earth science community that provides a mechanism for scientific collaboration, knowledge and data sharing together with direct access to over 1PB of Earth science data and 10,000-cores processing system. The cloud component will interface with NASA OpenNEX ' a cloud-based component of NEX. The project aligns well with number of goals of 'NASA's Plan for a Climate-Centric Architecture' and will be capable of supporting number of missions such as LDCM, OCO-2, or SMAP. There will be immediate benefit to number of existing and upcoming projects. The WELD (Web Enabled Landsat Data) project sponsored by NASA MEASUREs program will benefit immediately through improved production and QA monitoring capabilities as well as more efficient execution. There are also a number of projects that are ready to build on the WELD results. First of them is NASA GIBS, a core EOSDIS component, that requires to deliver native resolution imagery from WELD (this will be about 5PB production system). There are also science projects that hope to build on WELD results by implementing MODIS algorithms such as FPAR/LAI using the high-resolution Landsat data. In order to demonstrate the capabilities of the system, we will deploy a prototype on the existing NEX Landsat WELD processing system ' a complex 30-stage pipeline, which delivers derived vegetation products by processing over 1.5PB of data.

The project will be developed in several stages each addressing separate challenge ' workflow integration, parallel execution in either cloud or HPC environments and big-data analytics and visualization. We will first develop the capability and best practices to assist science teams with integration of their large-scale processing pipelines with the workflow system. We will continue with enabling users to launch seamless data production on either cloud or HPC environments, while tracking the data and process provenance. This effort will be based on previous ESTO-funded activities. Finally, we will integrate the system with web-base visualization tools to enable efficient big-data visualization and analytics of the results.

The period of performance of the project is two years and we have estimated the possible beginning for March 2015. However, the exact start date is not critical for this project and it can be readily adjusted.

We have estimated the entry TRL of the efforts at 4 and we will deliver a system with exit TRL of 6. The detailed TRL justification is provided in the proposal.

The Earth Observing System Data and Information System (EOSDIS) is a major core capability within NASA''s Earth Science Data Systems Program. EOSDIS ingests, processes, archives and distributes data from a large number of Earth observing satellites. EOSDIS consists of a set of processing facilities and Earth Science Data Centers distributed across the United States and serves hundreds of thousands of users around the world, providing hundreds of millions of data files each year covering many Earth science disciplines. In order to serve the needs of a broad and diverse community of users, NASA''s Earth Science Data Systems Program is comprised of both Core and Community data system elements. Core data system elements reflect NASA''s responsibility for managing Earth science satellite mission data characterized by the continuity of research, access, and usability. The core comprises all the hardware, software, physical infrastructure, and intellectual capital NASA recognizes as necessary for performing its tasks in Earth science data system management. Community data system elements are those pieces or capabilities developed and deployed largely outside of NASA core elements and are characterized by their evolvability and innovation. Successful applicable elements can be infused into the core, thereby creating a vibrant and flexible, continuously evolving infrastructure. NASA''s Earth Science program was established to use the advanced technology of NASA to understand and protect our home planet by using our view from space to study the Earth system and improve prediction of Earth system change. To meet this challenge, NASA promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public. NASA was the first agency in the US, and the first space agency in the world, to couple policy and adequate system functionality to provide full and open access in a timely manner - that is, with no period of exclusive access to mission scientists - and at no cost. NASA made this decision after listening to the user community, and with the background of the then newly-formed US Global Change Research Program, and the International Earth Observing System partnerships. Other US agencies and international space agencies have since adopted similar open-access policies and practices. Since the adoption of the Earth Science Data Policy adoption in 1991, NASA''s Earth Science Division has developed policy implementation, practices, and nomenclature that mission science teams use to comply with policy tenets. Data System Standards NASA''s Earth Science Data Systems Groups anticipate that effective adoption of standards will play an increasingly vital role in the success of future science data systems. The Earth Science Data Systems Standards Process Group (SPG), a board composed of Earth Science Data Systems stakeholders, directs the process for both identification of appropriate standards and subsequent adoption for use by the Earth Science Data Systems stakeholders.

Satvision Pretraining Dataset - Small

Developed by: NASA GSFC CISTO Data Science Group Model type: Pre-trained visual transformer model License: Apache license 2.0

This dataset repository houses the pretraining data for the Satvision pretrained transformers. This dataset was constructed using webdatasets to limit the number of inodes used in HPC systems with limited shared storage. Each file has 100000 tiles, with pairs of image input and annotation. The data has been further… See the full description on the dataset page: https://huggingface.co/datasets/nasa-cisto-data-science-group/legacy-satvision-sr-pretrain-small.

The NASA Earth Exchange (NEX) represents a new platform for the Earth science community that provides a mechanism for scientific collaboration and knowledge sharing. NEX combines state-of-the-art supercomputing, Earth system modeling, workflow management, NASA remote sensing data feeds, and a knowledge sharing platform to deliver a complete work environment in which users can explore and analyze large datasets, run modeling codes, collaborate on new or existing projects, and quickly share results among the Earth Science communities.

The NASA Science Information Policy for the Science Mission Directorate (SPD-41a) provides requirements for how scientific information produced from SMD funded scientific activities must be shared. SPD-41a requirements for research awards were incorporated into SMD’s Research Opportunities in Space and Earth Science (ROSES) starting with the ROSES-2023 solicitation. This dataset and notebooks include analysis of publications from 2023. This incudes: 1) analysis of publications for the percentage of publications that are open access 2) sampling of publications that make the data and software openly available.

This data is a companion to the full report that presents the results of the analysis.

Distribution of observations (in %) by data policy (public vs private), geometry (point vs polygon) and year of observation for datatype FO and CV (top) and datatype AC and FD (bottom).

Market Size and Growth: The Satellite Data Services Market is estimated to reach a value of USD 12.10 billion by 2033, exhibiting a CAGR of 16.0% from 2025 to 2033. This growth is driven by the increasing demand for satellite data for various applications, including defense and security, energy and utilities, and agriculture and forestry. Additionally, the rise of low-earth orbit (LEO) satellites and the adoption of cloud computing technologies are further fueling market expansion. Key Drivers, Trends, Restraints, and Segments: Key drivers of the Satellite Data Services Market include the growing adoption of precision agriculture, the need for real-time data for disaster management, and the rising investments in space-based technologies. Trends shaping the market include the increasing popularity of synthetic aperture radar (SAR) data and the emergence of AI-powered data analytics solutions. Potential restraints include concerns over data security and privacy, as well as the high cost of satellite data services. The market is segmented based on application, deployment, service, end use, and region. Defense and security, energy and utilities, and agriculture and forestry are the major application segments. Private and government and military are the largest deployment segments. Image data service and data analytics service are the key service segments. Commercial and government and military are the dominant end-use segments. North America and Europe are the most significant regional markets. The global satellite data services market size is estimated to be USD 14.2 billion in 2023 and is projected to reach USD 26.2 billion by 2030, exhibiting a CAGR of 8.1% during the forecast period. Recent developments include: In September 2024, Planet Labs Germany GmbH signed a three-year contract with the German Space Agency at the German Aerospace Center. The deal would enable Planet to offer its Earth observation data services and products to the German Space Agency, as well as German researchers to aid in their research and development activities. Researchers will be able to access the company’s PlanetScope products, which includes their advanced monitoring capabilities and a comprehensive archive of PlanetScope data. The German Space Agency will be additionally receiving Planet’s complete archive of RapidEye imagery over Germany. , In September 2024, ICEYE US announced that it had been selected by NASA on a 5-year contract to provide synthetic aperture radar data for the latter’s Commercial Smallsat Data Acquisition Program. This project has been leveraging commercial sources since 2020 to identify, evaluate, and acquire data to support the Earth Science Division of NASA in its scientific research and application objectives. .

The Web-enabled Landsat Data (WELD) project is collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and academic partner South Dakota State University Geographic Information Science Center of Excellence. It is funded by NASA's Making Earth System Data Records for Use in Research Environments, with significant USGS cost sharing.

The Web-enabled Landsat Data (WELD) project is collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and academic partner South Dakota State University Geographic Information Science Center of Excellence. It is funded by NASA's Making Earth System Data Records for Use in Research Environments, with significant USGS cost sharing.

NASA s Rodent Research (RR) project is playing a critical role in advancing biomedical research on the physiological effects of space environments. Due to the limited resources for conducting biological experiments aboard the International Space Station (ISS) it is imperative to use crew time efficiently while maximizing high-quality science return. NASA s GeneLab project has as its primary objectives to 1) further increase the value of these experiments using a multi-omics systems biology-based approach and 2) disseminate these data without restrictions to the scientific community. The current investigation assessed viability of RNA DNA and protein extracted from archived RR-1 tissue samples for epigenomic transcriptomic and proteomic assays. During the first RR spaceflight experiment a variety of tissue types were harvested from subjects snap-frozen or RNAlater-preserved and then stored at least a year at -80C after return to Earth. They were then prioritized for this investigation based on likelihood of significant scientific value for spaceflight research. All tissues were made available to GeneLab through the bio-specimen sharing program managed by the Ames Life Science Data Archive and included mouse adrenal glands quadriceps gastrocnemius tibialis anterior extensor digitorum longus soleus eye and kidney. We report here protocols for and results of these tissue extractions and thus the feasibility and value of these kinds of omics analyses. In addition to providing additional opportunities for investigation of spaceflight effects on the mouse transcriptome and proteome in new kinds of tissues our results may also be of value to program managers for the prioritization of ISS crew time for rodent research activities.

Confidence scores of all data sets (private and public).

Both the National Research Council (NRC) Decadal Survey and the latest Intergovernmental Panel on Climate Change (IPCC) Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global observations in order to maximize the investments made in Earth observational systems and also to capitalize on them for improving our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations.

We propose to develop a novel methodology to diagnose model biases in contemporary climate models and to implement the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system for the Earth science modeling and model analysis community. The proposed information system is named Climate Model Diagnostic Analyzer (CMDA) and will be built upon the current version of CMDA, which is the product of the research and technology development investments of several current and past NASA ROSES programs led by the proposal team members. We will leverage the current technologies and infrastructure of CMDA and extend the capabilities of CMDA to address several technical challenges that the modeling and model analysis community faces in evaluating climate models by utilizing three technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology.

The proposed diagnostic analysis methodology will help the scientists identify the physical processes responsible for creating model biases and incorporate the understanding into new model representations that reduce the model biases. Potentially, the results of the proposed work can significantly increase the model predictability of climate change because improving the model representations of the current climate system is essential to enhancing confidence in seasonal, decadal, and long-term climate projections.

Additionally, the proposed web-service based, cloud-enabled technology will facilitate a community-wide use and relatively effortless adoption of this novel model-diagnosis methodology. Its web-browser interface and cloud-based computing will allow instantaneous use without the hassle of local installation, compatibility issues, and scalable computational resource issues and offer a low barrier to the adoption of the tool.

Finally, the proposed provenance-supported technology will automatically keep track of processing history during analysis calls, represent the summary of the processing history in a human readable way, and enable provenance-based search capabilities. Scientists currently spend a large portion of their research time on searching previously analyzed results and regenerating the same results when they fail to locate them. The reproducibility of the analysis results by other scientists is also limited for the same reason. The proposed provenance support technology will greatly improve the productivity of the scientists using the analysis tool and enable scientists to share/reproduce the results generated by other scientists.

This layer shares SEDAC's population projections for U.S. counties for 2020-2100 in increments of 5 years, for each of five population projection scenarios known as Shared Socioeconomic Pathways (SSPs). This layer supports mapping, data visualizations, analysis and data exports.Before using this layer, read:The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview by Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni. Global Environmental Change, Volume 42, 2017, Pages 153-168, ISSN 0959-3780, https://doi.org/10.1016/j.gloenvcha.2016.05.009.From the 2017 paper: "The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature."According to SEDAC, the purpose of this data is:"To provide subnational (county) population projection scenarios for the United States essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications."According to Francesco Bassetti of Foresight, "The SSP’s baseline worlds are useful because they allow us to see how different socioeconomic factors impact climate change. They include: a world of sustainability-focused growth and equality (SSP1); a “middle of the road” world where trends broadly follow their historical patterns (SSP2); a fragmented world of “resurgent nationalism” (SSP3); a world of ever-increasing inequality (SSP4);a world of rapid and unconstrained growth in economic output and energy use (SSP5).There are seven sublayers, each with county boundaries and an identical set of attribute fields containing projections for these seven groupings across the five SSPs and nine decades.Total PopulationBlack Non-Hispanic PopulationWhite Non-Hispanic PopulationOther Non-Hispanic PopulationHispanic PopulationMale PopulationFemale PopulationMethodology: Documentation for the Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Data currency: This layer was created from a shapefile downloaded April 18, 2023 from SEDAC's Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Enhancements found in this layer: Every field was given a field alias and field description created from SEDAC's Data Dictionary downloaded April 18, 2023. Citation: Hauer, M., and Center for International Earth Science Information Network - CIESIN - Columbia University. 2021. Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/dv72-s254. Accessed 18 April 2023.Hauer, M. E. 2019. Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. https://doi.org/10.1038/sdata.2019.5.Distribution Liability: CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-465-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

The Cooperative Open Online Landslide Repository, or COOLR, is an open platform where scientists and citizen scientists can share landslide reports. See all landslide data from COOLR with other scientific data using the Landslide Viewer application. For more information: https://gpm.nasa.gov/landslides/index.html Landslides @ NASA is a project of NASA's Precipitation Measurement Missions (PMM) which use satellite data and citizen science data to model and inventory landslides around the world. We are based at NASA Goddard Space Flight Center (GSFC). Learn more about our publications and projects on the Resources page.

The Web-enabled Landsat Data (WELD) project is collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and academic partner South Dakota State University Geographic Information Science Center of Excellence. It is funded by NASA's Making Earth System Data Records for Use in Research Environments, with significant USGS cost sharing.

Develop a service-oriented hazard/disaster monitoring data system enabling both science and decision-support communities to monitor ground motion in areas of interest with InSAR and GPS.
Enable high-volume and low-latency automatic generation of NASA Solid Earth science data products (InSAR and GPS) to support hazards monitoring.
Enable improved understanding through visualization, mining, and cross-agency sharing of results.
Enable interoperable discovery, access, and sharing of derived actionable products for hazards monitoring.
 

In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA) in the Amazonas region for the time period 2003-2019. TWSAs were used as target (output) values of the neural network and were derived from the Land Surface Discharge Model (LSDM, Dill, 2008). The corresponding input values were calculated by spatially smoothing the TWSA fields with a 600 km Gaussian filter. After training the neural network over the time period of 2003 to 2018, its performance was tested and compared to LSDM for the subsequent year 2019.

JSC provides and applies its preeminent capabilities in science and technology to develop, operate, and integrate human exploration missions.  The center encourages collaboration with aerospace and non-aerospace industries, government agencies, and academia to solve science and technology challenges, while actively striving to maximize technology transfer into the commercial sector.

An active and sustainable science and technology development program is key to ensuring the challenges of human exploration are successfully overcome. The JSC-directed solicitations program enables the center to invest strategically in high priority areas needed to accomplish future missions, as articulated in the NASA Technology Roadmaps and the Space Technology Investment Plan (STIP). It offers the center the ability to address technology gaps that are beyond the requirements of near-term programs to fund.  The program also provides a platform to continue to grow and maintain critical skills and innovations needed to ensure future mission success.  These solicitations encourage use of collaborations to ensure maximum benefit to both the space program and the nation.  As such, external partnerships are highly encouraged not only as a funding leverage but to bring innovative ideas and approaches into human exploration programs.

Selection Process

Typically, JSC solicitations are developed by the JSC CTO and the JSC Technology Working Group (JTWG). Competitive calls are coordinated with JSC Senior Staff and communicated to the JSC workforce via internal email distribution to an R&D community list and through postings on the internal center website and through JSC Today notices.

The JTWG solicits, evaluates and prioritizes all JSC solicitation responses in a two-stage process. The JTWG members review project proposals and work together to down-select to the finalists. The Principal Investigators (PIs) make presentations to the JTWG to provide more in-depth project details. This allows the members to have multiple sets of data to select the most innovative finalists to support for the year.  Selection criteria and funding vary based on the focus of the solicitation; but of primary interest are:

• Human Space Flight Architecture Team (HAT) prioritized technology needs
• Priority JSC technology core competencies
• High potential areas for technology commercialization
• High potential areas for technology partnerships

Project Accomplishments

Through the result of research and development, JSC’s IR&D project PIs are making essential progress in the advancement of technology needed to enable NASA’s mission of space exploration. Additionally, many of the technologies developed to meet the challenges of space exploration have great commercialization potential. Each year, many of JSC’s IR&D projects file New Technology Reports (NTRs) through the JSC Tech Transfer Office. Several of these reports have received New Technology Evaluation Patent ratings to pursue patents, while additional ones have been scheduled for success story articles to be written and published.

JSC projects active in FY12 and beyond have been included in TechPort. Through the TechPort tool information on the projects is provided and will be updated by PIs as developments and updates become available. This will offer further knowledge and information sharing between NASA developers, researchers, engineers and scientists, and other internal and external stakeholders.

The size of the Commercial Earth Observation Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of XXX% during the forecast period. Commercial Earth Observation (EO) refers to the collection and analysis of satellite or airborne data for commercial purposes, such as mapping, agriculture, urban planning, environmental monitoring, and disaster response. Private companies provide high-resolution imagery, geospatial data, and analytics services to governments, businesses, and industries. These observations offer valuable insights into land use, climate change, infrastructure development, and resource management. The commercialization of EO data has made it more accessible and affordable, enabling companies to create innovative solutions and drive decision-making across various sectors. This growth is driven by several factors, including the increasing demand for geospatial data for decision-making, the growing adoption of cloud-based platforms and services, and the advancements in sensor and data processing technologies. Key applications of commercial earth observation include urban development, mapping and surveying, agriculture, environmental monitoring, natural resource exploration, security and intelligence, disaster and emergency management, and others (energy, maritime, etc.). Recent developments include: In February 2024, The National Geospatial-Intelligence Agency expanded its use of commercial satellite imagery and analytics through a new procurement program called "Luno." The Luno initiative aims to leverage commercial satellite imagery and data analytics to enhance NGA's global monitoring capabilities and demonstrate a tangible commitment to commercial earth observation services., In January 2024, the European Commission and the European Space Agency awarded Kuva Space a USD 5.3 million commercial contract to provide hyperspectral data services for the Copernicus program. Kuva Space will be the sole provider of hyperspectral data services for the Copernicus program. Kuva Space's constellation of 100 CubeSats aims to provide hyperspectral images of any location on Earth at visible-to-near infrared (VIS-NIR) and visible-to-shortwave infrared (VIS-SWIR) wavelengths., In November 2023, Tata Advanced Systems Ltd, a provider of aerospace and defense solutions, entered into a partnership with Satellogic Inc. with the aim of establishing and enhancing local space technology capabilities in India. TASL and Satellogic will collaborate on the development of a new satellite design. They will work together to integrate multiple payloads onto a single satellite, which will generate a diverse range of data over India., In October 2023, NASA selected seven companies to provide commercial data support for earth science research. The Commercial Smallsat Data Acquisition Program will provide NASA with earth observation data and related services from commercial sources. This fixed-price, indefinite-delivery/indefinite-quantity, multiple-award contract is effective for five years with an option to extend services for an additional six months. The maximum potential value of all contractors selected is $476 million., In September 2023, Smallsat developer Open Cosmos raised USD 50 million in order to expand the company and develop larger satellites and constellations focused on earth observation. The funding will allow the 70-person company to grow internationally, including in Latin America, the Middle East and the Asia Pacific. It will allow it to expand its current satellite offerings, which include CubeSats, to larger spacecraft and constellations. The company is also in the process of developing a satellite data analytics platform known as DataCosmos..