Recent advances in areas of image processing methods, cloud computing and machine learning techniques, has made it easier to access and process satellite data. This article makes the case for how this offers us a new repertoire of options for measuring impact.
Social impact measurement has significant practitioner interest from a wide range of actors including government, social entrepreneurs, third-sector enterprises and impact investors. However, the attempts at measuring social impact has been quite tumultuous, owing to proliferation in terminology and definitions, presence of diverse contexts and standards. For instance, a review of literature on social impact[1] identified varied use of terminologies such as social value, social return, social performance, social accounting and social return on investment. It is also complex to measure social impact as its definition and parameters are likely to vary depending on contexts such as education, health, environment, and poverty. Similarly, multilateral and national organization like United Nations, BCorp and Global Impact Investment Network (GIIN) have set out a number of varied parameters under the ambit of Global Reporting Initiative, B Impact Assessment, and Impact Reporting and Investment Standards (IRIS) respectively. Recent advances in areas of image processing methods, cloud computing and machine learning techniques, has made it easier to access and process satellite data. We make the case for how this offers us a new repertoire of options for measuring impact.
Why use satellite images for impact measurement?
The foremost benefit of satellite imaging for impact measures is its objective, independent, and impartial nature. It helps to overcome limitations of surveys or government census based impact measures which rely on subjective perceptions and self-reporting. Data is obtained remotely through satellite sensors, without any direct human intervention in the field. Second, field based measurement through surveys are complex to carry out, takes a lot of time and quite expensive. Thanks to the advances made in image processing methods, cloud computing and the availability of open access to large collections of data, satellite observations are easily accessible for processing in a matter of hours. This includes Google Earth Engine and The Open Data Cube which are open source and open access platforms that include satellite data collection libraries and processing capabilities. Third, satellite image based impact measurement are ideal for long-term impact measurement. For example, Google Earth Engine offers satellite data for over five decades, which allows to track impact on longitudinal basis, opening pathways for understanding historical evolution of impact. Finally, it offers a global scale for impact measurement, making it more suitable to analyse country-wide and cross-country impact measures.
Current areas of application
The aforementioned benefits has already led to satellite images being used in supporting impact delivery and measurement in contexts such as food security, water access, global warming and disaster management. For example, SatSure uses satellite remote sensing to offer support to farmers and related service providers in India to tackle food security and livelihood issues. At the Socially Progressive Innovation and Entrepreneurship (SPIE) doctoral centre at Strathclyde, we have been using satellite images and machine learning methods to predict probabilistically water point locations in Malawi. Open satellite imagery provided by DigitalGlobe offered critical insights for humanitarian relief efforts during hurricanes, cyclones and floods. Planet offers high resolution images for a variety of applications ranging from precise agriculture to assets monitoring and illegal deforestation and changes in land use.
What’s needed next?
[1] Rawhouser, H., Cummings, M., & Newbert, S. L. (2019). Social impact measurement: Current approaches and future directions for social entrepreneurship research. Entrepreneurship Theory and Practice, 43(1), 82-115.
Editor's Note:
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