Drone Data Bottlenecks
And How to Fix Them
Jeffrey K. Gillan
Research Data Scientist
jgillan@arizona.edu
Tyson L. Swetnam
Director of Open Science
tswetnam@cyverse.org
Tech & Research Initiative Fund (TRIF)
Animal Agriculture
Plant Agriculture
Construction
Infrastructure
Geology
Mining
Wildfire/Forestry
Ecosystem Monitoring
Disaster Management
Large Data Requirements
Meta-analysis of 204 peer-reviewed studies in Environmental Management
(Walker et al. 2023)
Top Barrier to Drone Use?
UAS data provide unique high spatiotemporal resolutions
UAS data is BIG
UAS Data Management is often ad hoc for each group
Wyngaard et al. 2019
Characteristics of Drone Data
Remote Sensing
Optical
MultiSpectral
Full motion video
Thermal
LiDAR
Hyperspectral
Synth. Aperture Radar
Drone Data Types
Other Data
Methane
Aerosol
Drone telemetry
Flight metadata
Digital Surface Model
Digital Terrain Model
Orthomosaic
Canopy Height Model
Derivative Products
Point Cloud
Drone Data LifeCycle
Data Collection
1
Data Sharing
5
Data Storage
4
Data Processing
2
Data Analysis
3
2024
A Survey of Drone Data Management - 55 Respondents
What is your most pressing UAS Data Need?
2024
Data Processing
Where do you Process your UAS Data?
2024
Data Processing
Current Processing Bottlenecks?
Data Processing
Processing Fixes
Expertise to help you use HPC and Cloud Computing
UofA HPC
2. Scripting
Data Processing
Processing Fixes
Metashape Scripting from Open Forest Observatory https://github.com/open-forest-observatory/automate-metashape
Free Metashape Licenses for Non-Commerical use https://github.com/jeffgillan/agisoft-metashape
Containerized Automation with OpenDroneMap
2024
Data Storage
Where do you store your UAS data?
Data Storage
Bottlenecks for Storing UAS Data?
Storage Volume - Not enough space
Storage Costs - Too expensive to host online
Input | Output - Too slow to read/write data
Storage Fixes
Drone Data Cloud Storage
Data Storage
To Give your Data a 2nd Life
So Colleagues can:
Reproduce, Build on, and Synthesize
Federal Policy1
1 https://www.whitehouse.gov/wp-content/uploads/2022/08/08-2022-OSTP-Public-Access-Memo.pdf
Data Sharing
Why Share Drone Data?
Drone Data Repositories
Data Sharing
Stream Data from Cloud Storage to Any App
View and Analyze without Downloading
Serverless!
Data Sharing
Cloud Native Formats
Cloud Native Formats
FlatGeobuff
GeoParquet
Data Sharing
https://github.com/ua-datalab/Geospatial_Workshops/wiki/Cloud-Optimized-Geotiffs
https://www.gillanscience.com/cloud-native-geospatial/cog/
https://github.com/ua-datalab/Geospatial_Workshops/wiki/Cloud-Optimized-Point-Clouds
https://github.com/ua-datalab/Geospatial_Workshops/wiki/Intro-to-Zarr-&-Xarray
Building a Federated Global Catalog
of Open Geospatial Data
Data Sharing
MetaData
Standard JSON Format
Standard API
Data Sharing
MetaData
Data Sharing
MetaData
Data Analysis
rasterio
Free to Use Tools
Data Analysis
Deep Learning Tools
DeepForest - Tree crown object-detection
Restor Foundation - Tree Crown instance & semantic segmentation
Detectree2 - Instance segmentation of tree crowns
Detecto - Object detection of many things (need to fine-tune)
Data Analysis
Reproducible Pipelines
Photogrammetry
Fir
Pine
Spruce
ML Species Identification
Individual Tree Detection
Python Library: https://py.d2s.org/ available in PyPI
QGIS Plugin: D2S Browser
Github Repository: https://github.com/gdslab/data-to-science
Developed by: Jinha Jung & Ben Hancock (Purdue University)
Open-source Web Platform for Drone Data
Store | SfM Process | Visualize | Share
ps2.d2s.org
QGIS Plugin: D2S Browser
Questions?
Comments?
Please Contact Me for Advice on Drone Data Management
Jeffrey K. Gillan
Data Science Institute
jgillan@arizona.edu
R. P. Abernathey et al., "Cloud-Native Repositories for Big Scientific Data," in Computing in Science & Engineering, vol. 23, no. 2, pp. 26-35, 1 March-April 2021, doi: 10.1109/MCSE.2021.3059437.
Barbieri, L, Wyngaard, J, Swanz, S and Thomer, AK. 2023. Making Drone Data FAIR Through a Community-Developed Information Framework. Data Science Journal, 22: 1, pp. 1–9. DOI: https://doi.org/10.5334/ dsj-2023-001. https://account.datascience.codata.org/index.php/up-j-dsj/article/download/dsj-2023-001/1154
Barnas, AF, Chabot, D, Hodgson, AJ, Johnston, DW, Bird, DM and Ellis-Felege, SN. 2020. A standardized protocol for reporting methods when using drones for wildlife research. Journal of Unmanned Vehicle Systems. Publisher: NRC Research Press. DOI: https://doi.org/10.1139/juvs-2019-0011
Eskandari R, Mahdianpari M, Mohammadimanesh F, Salehi B, Brisco B, Homayouni S. Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models. Remote Sens. 2020;12(21):3511
Fremand, Alice. Towards a data commons: Imagery and derived data from autonomous and remotely piloted aerial vehicles. UK Polar Data Centre, British Antarctic Survey This report is an output of Work Package 4 of the Environmental Data Service (EDS) UKRI DRI Phase 1b grant. November 2023 https://nora.nerc.ac.uk/id/eprint/536398/1/UAV_NERC_report.pdf
Guo J, Huang C, Hou J. A Scalable Computing Resources System for Remote Sensing Big Data Processing Using GeoPySpark Based on Spark on K8s. Remote Sensing. 2022; 14(3):521. https://doi.org/10.3390/rs14030521
James, MR, JH Chandler, A. Eltner, C. Fraser, PE Miller, JP Mills, T. Noble, S. Robson, and SN Lane. 2019. Guidelines on the use of structure-from-motion photogrammetry in geomorphic research. Earth Surface Processes and Landforms 44 (10), 2081-2084. https://doi.org/10.1002/esp.4637
Lachowiec, J., Feldman, M. J., Matias, F. I., LeBauer, D., & Gregory, A. (2024). Adoption of unoccupied aerial systems in agricultural research. The Plant Phenome Journal, 7(1), e20098.
La Salandra, M., Miniello, G., Nicotri, S., Italiano, A., Donvito, G., Maggi, G., ... & Capolongo, D. (2021). Generating UAV high-resolution topographic data within a FOSS photogrammetric workflow using high-performance computing clusters. International Journal of Applied Earth Observation and Geoinformation, 105, 102600.
Pereyra Irujo G, Bernaldo P, Velázquez L, Pérez A, Molina Favero C, Egozcue A (2023) Open Science Drone Toolkit: Open source hardware and software for aerial data capture. PLoS ONE 18(4): e0284184. https://doi.org/10.1371/journal.pone.0284184
Vithlani, H. N., Dogotari, M., Lam, O. H. Y., Prüm, M., Melville, B., Zimmer, F., & Becker, R. (2020, May). Scale Drone Mapping on K8S: Auto-scale Drone Imagery Processing on Kubernetes-orchestrated On-premise Cloud-computing Platform. In GISTAM (pp. 318-325).
Walker, S. E., Sheaves, M., & Waltham, N. J. (2023). Barriers to Using UAVs in Conservation and Environmental Management: A Systematic Review. Environmental Management, 71(5), 1052-1064. https://doi.org/10.1007/s00267-022-01768-8
Wilkinson, MD, Dumontier, M, Aalbersberg, IJ, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J-W, da Silva Santos, LB, Bourne, PE, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, CT, Finkers, R, Gonzalez-Beltran, A, Gray, AJG, Groth, P, Goble, C, Grethe, JS, Heringa, J, Hoen, PACT, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, ME, Mons, A, Packer, AL, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, S-A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, van der Lei, J, van Mulligen, E, Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J and Mons, B. 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3; 160018. DOI: https:// doi.org/10.1038/sdata.2016.18
Wyngaard, J.; Barbieri, L.; Thomer, A.; Adams, J.; Sullivan, D.; Crosby, C.; Parr, C.; Klump, J.; Raj Shrestha, S.; Bell, T. Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development. Remote Sens. 2019, 11, 1797. https://doi.org/10.3390/rs11151797
References
Open-source Web Platform for Drone Data
Store | SfM Process | Visualize | Share
ps2.d2s.org