Videos, photos, and AI-derived grain size data associated with “Modular AI and video surveys transform multiscale sediment grain size mapping”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript in preparation “Modular AI and video surveys transform multiscale sediment grain size mapping”. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, and 7) summaries of survey efficiency for all data. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models and 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models. In particular, the data package contains 110 folders and 179000 files. The files include 41 videos in .mov format, 63958 photos in .jpg format, 13541 video-derived photos in .png format, 12645 segmentation mask data in .tif format, 12645 segmentation data in .json format, 24625 .csv files that with metadata and grain size for each individual photo, 51534 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in “TableS1_Computational_efficiency.csv”. We thank the Confederated Tribes and Bands of the Yakama Nation for access to field locations where these photos were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.