# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 4 System Design and Implementation
### Data Collection  
Our methodology leverages nighttime light data from the VIIRS instrument aboard the Suomi-NPP satellite and various spectral indices derived from Sentinel-2 surface reflectance imagery, since it has been proven that both can be used for the successful detection of urbanization population density and economic activity. This has also been confirmed through our experimentation given in Section 5. The data are collected through Google Earth Engine (GEE), a cloud platform engineered by Google, that combines a huge catalog of satellite imagery from various sources, such as NOAA, NASA, and USGS. It also features preprocessed geospatial datasets that make large-scale analysis possible. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on Earth's surface [54]. Earth Engine is available for commercial use, while remaining free for academic and research use. We chose GEE over other platforms, such as Sentinel Hub, because of its ease of use, its clear documentation, and its huge user community. GEE also provides an API for easier integration with Python, which is our programming language of choice because of its data science libraries. Finally, the fact that GEE is capable of executing the majority of the needed computations on the cloud (albeit with some limitations) makes it ideal for the platform we envisioned, i.e., one that is accessible by anyone, without the need for a powerful computational system or abundance of storage. Finally, GEE is enhanced by contributions from the open-source community, with libraries such as _geemap_ and _emont_ which are the main tools we used. Using the eemont and Awesome Spectral Indices libraries, we can calculate the time series for the average radiance from the VIIRS DNB collection, NDVI, NDBI, EMBI, PISI, VgNIRBI, VrNIRBI spectral indices. We chose these indices because they have been used extensively in applications where the evaluation of vegetation and impervious surfaces over an area of interest is important. _emont_ extends the original GEE capabilities by addingautomation of various kinds, such as histogram matching for data fusion, panchromatic sharpening, and cloud masking. The prefecture of Magneesia and the city of Volos (where we are located) were selected as our study case area. Unfortunately, we were unable to extract ground-truth data regarding abandoned buildings for our study area, Volos municipality, Greece. Ground-truth data are essential to train and evaluate our machine learning model's performance. To accomodate this, we explored two solutions. The first one involves the collection and use of data from other regions. The second methodology focused on the acquisition of crowd-sourced data for not-abandoned buildings and a really small hand-labeled dataset for vacant ones. Both methodologies follow a similar structure based on the existing literature, where house and land vacancy are in general correlated with increased vegetation [4] as well as nighttime light radiance [55]. For our first method, we selected datasets from the city of Chicago that had ground-truth labels, as well as the exact geometry features of each building. Specifically, the Chicago dataset was pulled from the city's data portal and includes requests such as water quality reports, illegal construction, etc., and has been updated daily since the end of 2018. This dataset contained requests for abandoned buildings and vacant land; however, there was not a feature for not-abandoned buildings, so we assumed that requests related to water quality and illegal building were indications of their existence.