The objectives of the National Data Science Challenge (NDSC) are to:
Bring the tech community closer through working together and knowledge sharing
Provide an environment for the development of creative new ideas in Data Science
Equip students and professionals with essential technical skills and expertise to prepare them for Industry 4.0
Through this competition, we hope to showcase the ubiquity and usefulness of data in getting insights, thus reinforcing Indonesia’s emphasis on driving the digital economy and the use of big data.
The dataset contains about 100,000 satellite images of Northern Kenya in 10 frequency bands, collected by the International Livestock Research Institute. Local experts (pastoralists, or nomadic herders) manually labeled the forage quality at the corresponding geolocations—specifically, the number of cows from {0, 1, 2, 3+} that the location at the center of the satellite image can feed. Each satellite image is 1.95km across, and each pixel in it represents a 30 meter square. Pastoralists estimate the forage quality within 20 meters when they stand on location, an area slightly larger than a pixel in the full 65×65-pixel satellite image. The satellite images thus provide a lot of extra context, which may prove useful since forage quality is correlated across space. The challenge is to learn a mapping from a satellite image to forage quality so we can more accurately predict drought conditions. Furthermore, the current labeling is very sparse, and we want dense predictions of forage quality at any pixel in a satellite image, not just at the center.
# there may be questions from installer. Refer to Howto Install Redmine on Ubuntu www.redmine.org/projects/redmine/wiki/howto_install_redmine_on_ubuntu_step_by_step for explanation
# Next step is to upgrade all gems. It will take sometime to update all gems.
gem update
# possible error: connection problem.
# example erro message: Updating roadie
ERROR: Error installing roadie:
Unable to resolve dependency: user requested ‘roadie (= 3.5.0)’
Facebook has just introduced a great dataset and corresponding machine learning challenge at same time. The dataset is named LVIS (Large Vocabulary Instance Segmentation).
Today, rigorous evaluation of general purpose object detectors is mostly performed in the few category regime (e.g. 80) or when there are a large number of training examples per category (e.g. 100 to 1000+). LVIS provides an opportunity to enable research in the setting where there are a large number of categories and where per-category data is sometimes scarce. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge.
