Datasets and Other Project Artifacts

Ground Truth Land Use Map

This is the ground truth land use map used for evaluation in our 2019 IEEE Transacations on Multimedia paper "Fine-grained land use classification at the city scale using ground-level images." ([pdf]) That work investigates the problem of mapping land use through the automated analysis of large collections of ground-level images. Since there is no ground truth available at the fine level of classes that we want, we create our own using points of interest (POIs) as indexed by Google Places. This map is therefore in some ways a surrogate ground truth map since it isn't created manually. There are 33 land use classes arranged in a three level hierarchy.

There are three files associated with this dataset:

Please cite our 2019 IEEE Transacations on Multimedia paper if you use this dataset:

Y. Zhu, X. Deng, and S. Newsam, "Fine-grained land use classification at the city scale using ground-level images," IEEE Transactions on Multimedia, 21(9), pp. 1825-1838, 2019.

Activity Recognition Models

This project investigates georeferenced videos for geographic knowledge discovery. For example, our 2017 ACM SIGSPATIAL paper "Large-scale mapping of human activity using geo-tagged videos" ([pdf]) perfoms activity detection in a large collection of georeferenced videos and then maps the results. We have developed several novel action recognition models in the context of this problem. Below, we provide two trained models as either Caffe or PyTorch implementations. They are described in the context of the papers that introduced them.