Here you can find information on use cases and user stories on the Geospatial Computing Platform.
Urban change detection plays a critical role in many domains such as city planning, infrastructure development, risk assessment, and land-use planning. However, the accurate classification of different types of changes in a 3D urban environment remains a challenging task. This study to address these challenges by implementing and evaluating three models: Random Forest, Fully Connected Neural Network, and Convolution Neural Network.
Climate extremes pose a threat to terrestrial ecosystem carbon sequestration, imperiling the EU's aim of achieving climate neutrality by 2050. The creation of an open digital twin of the soil-plant system serves to monitor and forecast the repercussions of extreme events on ecosystem functionality.
In the KAPPA project, we applied Self-Orgamized Maps (SOMs) to visualize the spatial relationship between allergy (hay fever) medication sales and pollen emissions in the Netherlands. SOM results were mapped to the corresponding geolocation to understand the spatial pattern.
Main objective of this study is to create citywide maps and spatial data based on physical characteristics of urban fabric (block layouts and roads) which can be translated using geospatial analytics to understand the quality of urban expansion.
This research aims to develop a dense image matching method, one of the key steps in the photogrammetric pipeline that transforms overlapping views of images into dense 3D information, based on an unsupervised deep network utilizing the parallax attention mechanism. The proposed approach effectively extracts dense point cloud from very high resolution UAV images.
Image colorization is the process of adding color to gray-scale images. To automate this process, colorization models using machine learning techniques are used, which are trained on large image sets. We used 2% of the images in ImageNet, a dataset of over 14 million images which was used to train the image colorizer DeOldify by using the Geospatial Computing Platform.
Our research aimed to increase the applicability of physically-based models in data-poor regions by improving the spatial resolution of globally available datasets by using deep learning-based Super Resolution. We used the high-resolution digital elevation models from Austria to train the Super Resolution models on the Geospatial Computing Platform of ITC.