Astrogeology is a world leader in developing and releasing analysis-ready planetary science data sets that are approachable and usable by anyone. We take care of all of the processing and provide data that "just work" so that users can focus on what interests them instead of getting stuck on data processing.
Documentation
Browse Data
Map-Based Search
Tutorials
Search API
PSDI
Analysis Ready Data Definition and Purpose
Analysis ready data (ARD) are data that are ready for immediate use in a Geographical Information System (GIS) or other data analysis tools. They have been processed to some additional degree, including the generation of higher-order data products such as digital terrain models (DTMs) or model-derived output, and therefore the data processing barrier for individual users is significantly reduced.
With this service, we enable planetary data to be more easily discoverable and accessible in a format that is ready for immediate use. This service allows individuals to discover and utilize discrete observations or derived products, and for existing services and portals to consume data via this service through a well-documented Application Programming Interface (API). As planetary data evolves and the volume of data collected continues to increase, we expect there will be ever-increasing need for available data to be processed using standard and well-documented methods. Also, as the community migrates data to cloud-based services, we have an opportunity to make these data more accessible and interoperable.
Why Analysis Ready Data?
Most publicly available planetary data are not analysis-ready, meaning that individuals must download raw or reduced data from mission websites or the Planetary Data System (PDS; https://pds.nasa.gov/) , radiometrically and geometrically calibrate and photogrammetrically correct the data, and often using multiple software packages before analysis can begin. This effort is significant and requires individuals to develop specialized processing and tool knowledge unrelated to their scientific, engineering, educational, or other expertise and for each dataset of interest. In contrast, the terrestrial community typically has access to analysis-ready data products that are ready for immediate use in a GIS or other data analysis tools (e.g., ( Citation: Giuliani, Chatenoux & al., 2017 ; Citation: Dwyer, Roy & al., 2018 ; Citation: Truckenbrodt, Freemantle & al., 2019 ; Citation: Egorov, Roy & al., 2019 ; Citation: Frantz, 2019 ; Citation: Zhu, 2019 ; Citation: Arribas-Bel, Green & al., 2021 ; Citation: Potapov, Hansen & al., 2020 ; Citation: Chatenoux, Richard & al., 2021 ) ). This broad access to analysis-ready data significantly reduces redundant data processing efforts.
Benefits of ARD
-
Data democritization. Data democritization is not just making data accessible (many of the data products linked within this service are already freely available in some form). Rather, data democritization is the ongoing process to make data discovery and use achievable regardless of a user’s technical skill set. This includes empowering users to be comfortable making informed, data driven decisions and asking questions about the data they are using. (Data-led Academy, 2021) The democritization of planetary data is our primary driver.
-
Through the democritization of planetary data, many secondary and tertiary benefits are realized:
-
Broad access to analysis-ready data significantly reduces costly redundant data processing efforts.
-
Reduced burden of learning data processing for a new data set; this burden is disproportionately felt by those new to a field, those not associated with mission teams, or in underserved communities.
-
Non-trivial increase in scientific output, as time and effort used to process data could instead be used for analysis and investigations.
-
Improved data access through fostered collaboration and sharing of data across the planetary community.
-
Improved innovative and cross-discipline science by making data from many disciplines available in an ARD format.
-
- Arribas-Bel, D., Green, M., Rowe, F. & Singleton, A. (2021). Open data products-A framework for creating valuable analysis ready data. Journal of Geographical Systems, 23(4). 497–514. https://doi.org/10.1007/s10109-021-00363-5
- Chatenoux, B., Richard, J., Small, D., Roeoesli, C., Wingate, V., Poussin, C., Rodila, D., Peduzzi, P., Steinmeier, C., Ginzler, C., Psomas, A., Schaepman, M. & Giuliani, G. (2021). The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Scientific Data, 8(1). 295. https://doi.org/10.1038/s41597-021-01076-6
- Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H. & Lymburner, L. (2018). Analysis Ready Data: Enabling Analysis of the Landsat Archive. Remote Sensing, 10(9). https://doi.org/10.3390/rs10091363
- Egorov, A., Roy, D., Zhang, H., Li, Z., Yan, L. & Huang, H. (2019). Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring. Remote Sensing, 11(4). https://doi.org/10.3390/rs11040447
- Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11(9). https://doi.org/10.3390/rs11091124
- Giuliani, G., Chatenoux, B., Bono, A., Rodila, D., Richard, J., Allenbach, K., Dao, H. & Peduzzi, P. (2017). Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD). Big Earth Data, 1(1-2). 100–117. https://doi.org/10.1080/20964471.2017.1398903
- Potapov, P., Hansen, M., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina, A. & Ying, Q. (2020). Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping. Remote Sensing, 12(3). https://doi.org/10.3390/rs12030426
- Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C., Thiel, C., Rossi, C., Syriou, A. & Giuliani, G. (2019). Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube. Data, 4(3). https://doi.org/10.3390/data4030093
- Zhu, Z. (2019). Science of Landsat Analysis Ready Data. Remote Sensing, 11(18). https://doi.org/10.3390/rs11182166
Astrogeology is a world leader in developing and releasing analysis-ready planetary science data sets that are approachable and usable by anyone. We take care of all of the processing and provide data that "just work" so that users can focus on what interests them instead of getting stuck on data processing.
Documentation
Browse Data
Map-Based Search
Tutorials
Search API
PSDI
Analysis Ready Data Definition and Purpose
Analysis ready data (ARD) are data that are ready for immediate use in a Geographical Information System (GIS) or other data analysis tools. They have been processed to some additional degree, including the generation of higher-order data products such as digital terrain models (DTMs) or model-derived output, and therefore the data processing barrier for individual users is significantly reduced.
With this service, we enable planetary data to be more easily discoverable and accessible in a format that is ready for immediate use. This service allows individuals to discover and utilize discrete observations or derived products, and for existing services and portals to consume data via this service through a well-documented Application Programming Interface (API). As planetary data evolves and the volume of data collected continues to increase, we expect there will be ever-increasing need for available data to be processed using standard and well-documented methods. Also, as the community migrates data to cloud-based services, we have an opportunity to make these data more accessible and interoperable.
Why Analysis Ready Data?
Most publicly available planetary data are not analysis-ready, meaning that individuals must download raw or reduced data from mission websites or the Planetary Data System (PDS; https://pds.nasa.gov/) , radiometrically and geometrically calibrate and photogrammetrically correct the data, and often using multiple software packages before analysis can begin. This effort is significant and requires individuals to develop specialized processing and tool knowledge unrelated to their scientific, engineering, educational, or other expertise and for each dataset of interest. In contrast, the terrestrial community typically has access to analysis-ready data products that are ready for immediate use in a GIS or other data analysis tools (e.g., ( Citation: Giuliani, Chatenoux & al., 2017 ; Citation: Dwyer, Roy & al., 2018 ; Citation: Truckenbrodt, Freemantle & al., 2019 ; Citation: Egorov, Roy & al., 2019 ; Citation: Frantz, 2019 ; Citation: Zhu, 2019 ; Citation: Arribas-Bel, Green & al., 2021 ; Citation: Potapov, Hansen & al., 2020 ; Citation: Chatenoux, Richard & al., 2021 ) ). This broad access to analysis-ready data significantly reduces redundant data processing efforts.
Benefits of ARD
-
Data democritization. Data democritization is not just making data accessible (many of the data products linked within this service are already freely available in some form). Rather, data democritization is the ongoing process to make data discovery and use achievable regardless of a user’s technical skill set. This includes empowering users to be comfortable making informed, data driven decisions and asking questions about the data they are using. (Data-led Academy, 2021) The democritization of planetary data is our primary driver.
-
Through the democritization of planetary data, many secondary and tertiary benefits are realized:
-
Broad access to analysis-ready data significantly reduces costly redundant data processing efforts.
-
Reduced burden of learning data processing for a new data set; this burden is disproportionately felt by those new to a field, those not associated with mission teams, or in underserved communities.
-
Non-trivial increase in scientific output, as time and effort used to process data could instead be used for analysis and investigations.
-
Improved data access through fostered collaboration and sharing of data across the planetary community.
-
Improved innovative and cross-discipline science by making data from many disciplines available in an ARD format.
-
- Arribas-Bel, D., Green, M., Rowe, F. & Singleton, A. (2021). Open data products-A framework for creating valuable analysis ready data. Journal of Geographical Systems, 23(4). 497–514. https://doi.org/10.1007/s10109-021-00363-5
- Chatenoux, B., Richard, J., Small, D., Roeoesli, C., Wingate, V., Poussin, C., Rodila, D., Peduzzi, P., Steinmeier, C., Ginzler, C., Psomas, A., Schaepman, M. & Giuliani, G. (2021). The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Scientific Data, 8(1). 295. https://doi.org/10.1038/s41597-021-01076-6
- Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H. & Lymburner, L. (2018). Analysis Ready Data: Enabling Analysis of the Landsat Archive. Remote Sensing, 10(9). https://doi.org/10.3390/rs10091363
- Egorov, A., Roy, D., Zhang, H., Li, Z., Yan, L. & Huang, H. (2019). Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring. Remote Sensing, 11(4). https://doi.org/10.3390/rs11040447
- Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11(9). https://doi.org/10.3390/rs11091124
- Giuliani, G., Chatenoux, B., Bono, A., Rodila, D., Richard, J., Allenbach, K., Dao, H. & Peduzzi, P. (2017). Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD). Big Earth Data, 1(1-2). 100–117. https://doi.org/10.1080/20964471.2017.1398903
- Potapov, P., Hansen, M., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina, A. & Ying, Q. (2020). Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping. Remote Sensing, 12(3). https://doi.org/10.3390/rs12030426
- Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C., Thiel, C., Rossi, C., Syriou, A. & Giuliani, G. (2019). Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube. Data, 4(3). https://doi.org/10.3390/data4030093
- Zhu, Z. (2019). Science of Landsat Analysis Ready Data. Remote Sensing, 11(18). https://doi.org/10.3390/rs11182166