A ResNeSt (ResNet based architecture with Split Attention) image classification model.
Trained on ImageNet-1k and fine-tuned on gold standard annotations and outputs from early experiments using MapReader (found
here).
This fine-tuned version of the model is an output of the MapReader pipeline.
It was used to classify 'patch' images (cells/regions) of scanned nineteenth-century series maps of Britain provided by the National Library of Scotland (learn more
here).
We classified patches to indicate the presence of buildings and railway infrastructure.
See
our ACM SIGSPATIAL Geohumanities Workshop 2022 paper for more details about labels.
Of all the models fine-tuned for the experiments reported in our ACM SIGSPATIAL 2022 paper, we used this resnest101e_timm_pretrain model to infer these railway infrastructure and building labels across 30 million patches on nearly 16k scanned map sheets. You can visualize the results of this work
here.
Please go to
the MapReader documentation for instructions on how to use this model in MapReader.
This model was fine-tuned on
manually-annotated data.
Open access version of the article available
here.
Data outputs can be found
here.
Further details can be found
here.
This model was fine-tuned using MapReader.
The code for MapReader can be found
here and the documentation can be found
here.
This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1).
Living with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and Cambridge, King's College London, East Anglia, Exeter, and Queen Mary University of London.
