In the last installment of this blog series on the value of Earth observation (EO) data, we focused on government supplied satellite data and its use in progressing global development solutions. Government EO data are predominately characterized by high to low spatial resolution, a consistent revisit rate, constant imaging that allows for routine monitoring, and, a deep archive of data to be built up and relied upon (United Nations, 2017, p. 42 and Green et al., 2017, p. 51). In the case of the Landsat and Sentinel programs, the data are open and free to use and hosted on multiple sites globally. These satellites tend to be large, very expensive to build and launch, and can take a decade to go from design to launch and then to operations.
Understanding our world and the interconnectedness of the natural and built environment is a great challenge to global development professionals as well as scientists and technologists. The role that Earth observation (EO) plays in this understanding is difficult to put in terms of economic value. However, to capture the greatest use from EO, our community is continually challenged to make this information much more accessible and ready for analysis, enabling data-driven development.
Advances in sensor technology, cloud computing, and machine learning (ML) continue to converge to accelerate innovation in the field of remote sensing. However, fundamental tools and technologies still need to be developed to drive further breakthroughs and to ensure that the Global Development Community (GDC) reaps the same benefits that the commercial marketplace is experiencing.
People have been viewing pictures of Earth since the 1840s, when cameras were strapped to balloons and kites. Technology has advanced significantly since then and today we are taking more images than ever before, using highly sophisticated instruments.
These days, the hot topic in the space industry is about revolution in satellite industry and democratization of space. What do we understand from that?