Drone data & the semantic web

Transcription

1 Drone data & the semantic web Lindsay Barbieri University of Vermont Dr. Jane Wyngaard University of Notre Dame Dr. Andrea Thomer University @an_dre_a_ PhD candidate, Natural Resources, Agriculture, Biogeochemistry Electrical Engineer, Data Technologist, Technology for Earth Science and Agriculture Assistant Professor, School of Information. Data curation, information organization, NHMs

2 Drone data & the semantic web Lindsay Barbieri Andrea Thomer Jane Wyngaard with thanks to Chuck Vardeman Beth Huffer Lewis Mcgibbney

3 Outline 1. Motivation & Background a. b. ESIP Drone Cluster RDA suas Data IG 2. suas scientific data challenges a. b. suas data is unique 10 challenges 3. ESIP Minimal Information Framework project (progress so far) a. b. c. Case Studies Ontologies MIF 4. Future a. b. ESIP Summer IDW

4 Outline 1. Motivation & Background a. b. ESIP Drone Cluster RDA suas Data IG 2. suas scientific data challenges a. b. suas data is unique 10 challenges 3. ESIP Minimal Information Framework project (progress so far) a. b. c. Case Studies Ontologies MIF 4. Future a. b. ESIP Summer IDW

5 Motivation & Background: ESIP 1. Earth Science Information Partners ( ESIP ) CONNECTING SCIENCE, DATA AND USERS 2. ESIP Drone Cluster ( Open Science Framework ) history: a. b. c. Integrating sensors on drones for biogeoscience project Standardized Embedded Data infrastructure for Drones (SEDD) Summer suas Data Workshop/Hackathon (presentations)

6 Motivation & Background: Data challenges from Barbieri, Wyngaard, Thomer et al., in prep 2018

7 Motivation & Background: Not just Earth Science from Barbieri, Wyngaard, Thomer et al., in prep 2018

8 Motivation & Background: RDA 1. Research Data Alliance ( RDA ) Research Data Sharing without barriers 2. RDA suas Data Interest Group ( suas Data IG ) history: a. Drones: Emerging Scientific Tools of the Trade ( Report ) b. Communities: Atmospheric Sciences (NCAR / ISARRA), Remote Sensing and Spectral sensors (OPTIMISE), Underwater Unmanned Vehicles (UUV), Academic Drone Labs (ESI), Standards Bodies (OGC), Agriculture, NGOs (UAViators, WildLabs)

9 suas Scientific Data Challenges 1. What standard sensor calibration and use procedures need to be defined and articulated? 2. What best practices regarding data post processing and error analysis methodology need to be outlined? 3. What is the minimum information that needs to be collected about a scientific suas data capture flight? 4. Which formats should be used to store (meta)data in? 5. Which ontologies should be applied -- or need to be developed -- for suas (meta)data? (what we began addressing at the VOCamp)

10 suas Scientific Data Challenges Why Care? Why Now?

11 suas Scientific Data Challenges Why Care? - Good Science! Understand & reduce uncertainty, important for science outcomes Sharing! Reproducible and reusable Quicker, Better Science! Increased learning and more rapid best practices science development Why Now?

12 suas Scientific Data Challenges Why Care? - Good Science! Understand & reduce uncertainty, important for science outcomes Sharing! Reproducible and reusable Quicker, Better Science! Increased learning and more rapid best practices science development Why Now? - Urgent! suas are an increasingly used sensor platform for the sciences Momentum and support! Open science and FAIR data practices Possible! Maturing of the technologies to enable and implement practices

13 suas Scientific Data Challenges 1. What standard sensor calibration and use procedures need to be defined and articulated? 2. What best practices regarding data post processing and error analysis methodology need to be outlined? 3. What is the minimum information that needs to be collected about a scientific suas data capture flight? 4. Which formats should be used to store (meta)data in? 5. Which ontologies should be applied -- or need to be developed -- for suas (meta)data? (what we began addressing at the VOCamp)

14 suas Scientific Data Challenges Okay, Real Talk!

15 Pre Flight Flight Post Flight

16 Pre Flight Flight Post Flight

17 Pre Flight Mission Planning / In field Executing Mission: Flight & Data Collection) Download in Field Data / Streaming Flight compare with field points Post Flight Barbieri, Wyngaard et al., in prep

18 suas Scientific Data Challenges 1. What standard sensor calibration and use procedures need to be defined and articulated? 2. What best practices regarding data post processing and error analysis methodology need to be outlined? 3. What is the minimum information that needs to be collected about a scientific suas data capture flight? 4. Which formats should be used to store (meta)data in? 5. Which ontologies should be applied -- or need to be developed -- for suas (meta)data? (what we began addressing at the VOCamp)

19 suas Scientific Data Challenges 1. What standard sensor calibration and use procedures need to be defined and articulated? 2. What best practices regarding data post processing and error analysis methodology need to be outlined? 3. What is the minimum information that needs to be collected about a scientific suas data capture flight? 4. Which formats should be used to store (meta)data in? 5. Which ontologies should be applied -- or need to be developed -- for suas (meta)data? (what we began addressing at the VOCamp)

20 ESIP Minimal Information Framework project (progress so far) Minimum information framework: a list* of data and metadata attributes necessary for sharing and reuse Project goals: 1. Define a high-level minimum information framework (MIF) for drone data based on case studies 2. Use MIF as backbone/testbed for preliminary drone data ontology A first step towards achieving FAIRness is to both augment them with machine-readable, semantically-rich metadata, and to annotate them in ways that make their provenance (the record of the processes that created the data) explicit.

21 ESIP Minimal Information Framework project (progress so far) Project steps: 1. Develop case studies of suas data research workflows 2. Collect - and where necessary, develop appropriate ontologies for all stages 3. Describe and publish for community comment a Minimal Information Framework for suas data A first step towards achieving FAIRness is to both augment them with machine-readable, semantically-rich metadata, and to annotate them in ways that make their provenance (the record of the processes that created the data) explicit.

22 Collecting and analyzing scientific RPAS workflows

23 Collecting and analyzing scientific RPAS workflows

24 Collecting and analyzing scientific RPAS workflows

25 Minimum information framework (so far) Wyngaard, J., Barbieri, L. K., Vardeman II, C., Leahy, B., Swanz, S., Thomer, A.K. (2018). Minimal Information Framework for Scientific Data Collection from Remotely Piloted Aircraft Systems (RPAS). Poster presented at 11th plenary of the Research Data Alliance. Berlin. doi: /m9.figshare *not a list

26 VOCamp: Ontologies to build on Geolink (ontology design pattern) W3C SOSA Various IEEE UAV/Robot ontologies Format candidates Onboard, web accessible: CoverageJSON Archive: NetCDF

27 VOCamp: ontology design pattern for flight

28 Final thoughts on potential value of such A suas data river is coming (already here?) We could make it A FAIR data river* democratising access to GIS data enabling eg: *Again FAIR does not necessarily mean open and the ethical considerations of such must be fully explored and accounted for. Scientific breakthroughs through data reuse and interoperability Improve agriculture efficiencies in changing climatic ecosystems Open the door to communities self surveying/using research or government data to demand infrastructure/prove land use/reveal corruption/prove environmental gains (for aid/trade verification) or injustices (regulation violations)... Enable citizen science suas contributions of quality data

29 Ongoing and Future - RDA suas Data Interest Group - - International Data Week Digital Frontiers of Global Science - 29 Oct - 9 Nov 2018, Gabarone Botswana - Flying Week - Drone Datathon Day - SciDataCon: - Call for abstracts due 30 April: submit here on SciDataCon site - Cross disciplinary drones for good in the developing world - New and Emerging Technologies in Agriculture: SciDataCon Session ESIP Drone Cluster Summer meeting July 17-20, 2018 in Tucson, AZ