Tsetse fly wing landmark data for morphometrics (Vol 20, 21)

Single-wing images were captured from 14,354 pairs of field-collected tsetse wings of species Glossina pallidipes and G. m. morsitans and analysed together with relevant biological data. To answer research questions regarding these flies, we need to locate 11 anatomical landmark coordinates on each wing. The manual location of landmarks is time-consuming, prone to error, and simply infeasible given the number of images. Automatic landmark detection has been proposed to locate these landmark coordinates. We developed a two-tier method using deep learning architectures to classify images and make accurate landmark predictions. The first tier used a classification convolutional neural network to remove most wings that were missing landmarks. The second tier provided landmark coordinates for the remaining wings. For the second tier, compared direct coordinate regression using a convolutional neural network and segmentation using a fully convolutional network. For the resulting landmark pred..., This data was collected via field traps designed to catch tsetse flies. The fly wings were processed from the flies and laminated on an A4 sheet of paper along with various biological recordings from a lab dissection of the fly. This data was subsequently digitised by recording the data for each fly in excel spreadsheets. A microscope camera was used to capture a digital image of the fly wings at a resolution of 1024×1280. A subset of images was annotated and used to train machine learning models. The wing images were then given as inputs to machine learning models which located and recorded various landmarks in each fly wing image. These landmarks were appended to the dataset of biological recording taken during the lab dissection. This data was processed to remove outliers and other erroneous instances in the data set.
The different files in the dataset are described below. 
tetse_data.csv
Column names and description

vpn: vpn is the filename, identified by the volume (v), page (p), ...,