This paper discusses the potential and limitations of the Normalized Difference Vegetation Index (NDVI) in environmental justice, health and inequality studies in urban areas. Very often the NDVI is correlated with socioeconomic and/or sociodemographic data to demonstrate the inequality in environmental settings that themselves influence individual health and questions of environmental justice. This paper addresses the limits of the NDVI for such applications and as well its potential, if applied properly. The overall goal is to make people of disciplines other than those that are geo-related aware of the characteristics, limits and potentials of satellite image-based information layers such as NDVI.

Light detection and ranging (LiDAR) and digital terrain models (DTM) revolutionized archeological prospection in the last two decades. Using the new technique, comprehensive areal detections of archeological relief structures (field monuments) hidden under dense vegetation became possible and archeologists found new sites even in well-known areas. In times of Open Geodata policies, archeologists have access to geospatial data sets such as DTM. Assessing its full potential requires automated workflows, which is a recent research topic in archeological research. However, all approaches, both manually and automated, are affected by misclassifications caused by confusions of archeological and modern structures. Digital landscape models (DLM) help differentiating structures by their location. Concerning these data, only 74% of the total area of Westphalia and Lippe need archeological investigation, increasing precision of automated classification approaches.

This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.

Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis.

Economic forecasts are an important instrument to judge the nation-wide economic situation. Such forecasts are mainly based on data from statistical offices. However, there is a time lag between the end of the reporting period and the release of the statistical data that arises for instance from the time needed to collect and process the data. To improve the forecasts by reducing the delay, it is of interest to find alternative data sources that provide information on economic activity without significant delays. Among others, satellite images are thought to assist here. This paper addresses the potential of earth observation imagery for short-term economic forecasts. The study is focused on the estimation of investments in the construction sector based on high resolution (HR) (10–20 m) and very high resolution (VHR) (0.3–0.5 m) images as well as on the estimation of investments in agricultural machinery based on orthophotos (0.1 m) simulating VHR satellite imagery. By applying machine learning it is possible to extract the objects of interest to a certain extent. For the detection of construction areas, VHR satellite images are much better suited than HR satellite images. VHR satellite images with a ground resolution of 30–50 cm are able to identify agricultural machinery. These results are promising and provide new and unconventional input for economic forecasting models.