Super fun. We at IGIS and the Kellylab are working with Drone Scholars on the #Fly4Fall project. Fly4Fall: A citizen science experiment for crowd sourcing UAV data.
Consider being a participant! Here are the contributions so far:
Super fun. We at IGIS and the Kellylab are working with Drone Scholars on the #Fly4Fall project. Fly4Fall: A citizen science experiment for crowd sourcing UAV data.
Consider being a participant! Here are the contributions so far:
Many of us have watched in horror and sadness over the previous week as fires consumed much of the beautiful hills and parts of the towns of Napa and Sonoma Counties. Many of us know people who were evacuated with a few minutes’ notice - I met a retired man who left his retirement home with the clothes on his back. Many other friends lost everything - house, car, pets. It was a terrible event - or series of events as there were many active fires. During those 8+ days all of us were glued to our screens searching for up-to-date and reliable information on where the fires were, and how they were spreading. This information came from reputable, reliable sources (such as NASA, or the USFS), from affected residents (from Twitter and other social media), and from businesses (like Planet, ESRI, and Digital Globe who were sometimes creating content and sometimes distilling existing content), and from the media (who were ofen using all of the above). As a spatial data scientist, I am always thinking about mapping, and the ways in which geospatial data and analysis plays an increasingly critical role in disaster notification, monitoring, and response. I am collecting information on the technological landscape of the various websites, media and social media, map products, data and imagery that played a role in announcing and monitoring the #TubbsFire, #SonomaFires and #NapaFires. I think a retrospective of how these tools, and in particular how the citizen science aspect of all of this, helped and hindered society will be useful.
In the literature, the theoretical questions surrounding citizen science or volunteered geography revolve around:
Accuracy – how accurate are these data? How do we evaluate them?
Access – Who has access to the data? Are their technological limits to dissemination?
Bias (sampling issues)/Motivation (who contributes) are critical.
Effectiveness – how effective are the sites? Some scholars have argued that VGI can be inhibiting.
Control - who controls the data, and how and why?
Privacy - Are privacy concerns lessened post disaster?
I think I am most interested in the accuracy and effectiveness questions, but all of them are important. If any of you want to talk more about this or have more resources to discuss, please email me: firstname.lastname@example.org, or Twitter @nmaggikelly.
Summary so far. This will be updated as I get more information.
Outreach from ANR About Fires
ANR has a number of programs dedicated to fire preparedness, recovery, and prevention.
Core Geospatial Technology During Fires
Fire perimeters from https://www.geomac.gov/services.shtml
The Active Fire Perimeters layer is a product of Geospatial Multi-Agency Coordination (GeoMAC). In order to give fire managers near real-time information, fire perimeter data is updated daily based upon input from incident intelligence sources, GPS data, infrared (IR) imagery from fixed wing and satellite platforms.”
MODIS hot spots from USFS Active Fire Mapping Program. MODIS The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. In addition to lots of other data, MODIS delivers Channel 31 brightness temperature (in Kelvins) of a hotspot/active fire pixel.
For more on how these are made: http://www.arcgis.com/home/item.html?id=b4ce4179b04f47e4ba79e234205565c1
Media using maps (super short list)
NYTimes: Minutes to Escape: How One California Wildfire Damaged So Much So Quickly. https://www.nytimes.com/interactive/2017/10/21/us/california-fire-damage-map.html?smid=fb-nytimes&smtyp=cur
NYTimes: How California’s Most Destructive Wildfire Spread, Hour by Hour. OCT. 21, 2017. https://www.nytimes.com/interactive/2017/10/21/us/california-fire-damage-map.html?smid=fb-nytimes&smtyp=cur
SFGate focusing on SkyIMD: http://www.sfgate.com/news/article/incredible-aerial-photos-wine-country-fires-12285123.php
Twitter: #sonomafires, #napafires, #tubbsfire
Flickr: SonomaFires, TubbsFires, NapaFires, etc.
Core Technology for Post-Fire Impact
High resolution imagery collection and analysis
Planet has made fire imagery available: https://www.planet.com/pulse/northern-california-wildfire-satellite-data-available-for-access/
Digital Globe + MapBox made a post-fire tool: (Author: @robinkraft
Email: email@example.com, Github repo, Source: Overview News // DigitalGlobe 2017): https://robinkraft.github.io/norcal-fires-imagery/compare.html; https://blog.mapbox.com/santa-rosa-fire-satellite-imagery-a31b6dfefdf8
Digital Globe - Open Data Program: https://www.digitalglobe.com/opendata
Sonoma Veg Map Program has a few links to interesting stuff including ArcGIS Server Drone and Digital Globe Imagery: http://sonomavegmap.org/blog/2017/10/17/fires/
I know SkyIMD was flying the entire time:
First Map - Flights 10/11-10/14/17 Here: http://www.skyimd.com/napa-sonoma-fire-imagery-map/
Second Map - Flight 10/20/17 - Here: http://www.skyimd.com/napa-sonoma-fire-imagery-map-2/
Greg Crutsinger from @droneScholars (https://www.dronescholars.com/) made this available: Aftermath of #TubbsFire: https://www.mapbox.com/bites/00382/#18/38.4763/-122.74861
Open Aerial Map has one drone image not sure the source https://map.openaerialmap.org/#/-122.7497720718384,38.471986484020334,16/square/023010201213/59e62be93d6412ef7220c4c0?_k=hm0j7l
Every fall I ask my GIS students to answer the big questions in advance of their class projects. This year climate change, wildlife conservation, land use and water quality are important, as well as a number of other topics. Remote sensing continues to be important to GISers. Scientists, government and communities need to work together to solve problems.
Here are the responses from Fall 2017:
UC ANR's IGIS program hosted 36 drone enthusiasts for a three day DroneCamp in Davis California. DroneCamp was designed for participants with little to no experience in drone technology, but who are interested in using drones for a variety of real world mapping applications. The goals of DroneCamp were to:
The IGIS crew, including Sean Hogan, Andy Lyons, Maggi Kelly, Robert Johnson, Kelly Easterday, and Shane Feirer were on hand to help run the show. We also had three corporate sponsors: GreenValley Intl, Esri, and Pix4D. Each of these companies had a rep on hand to give presentations and interact with the participants.
Day 1 of #DroneCamp2017 covered some of the basics - why drone are an increasingly important part of our mapping and field equipment portfolio; different platforms and sensors (and there are so many!); software options; and examples. Brandon Stark gave a great overview of the Univ of California UAV Center of Excellence and regulations, and Andy Lyons got us all ready to take the 107 license test. We hope everyone here gets their license! We closed with an interactive panel of experienced drone users (Kelly Easterday, Jacob Flanagan, Brandon Stark, and Sean Hogan) who shared experiences planning missions, flying and traveling with drones, and project results. A quick evaluation of the day showed the the vast majority of people had learned something specific that they could use at work, which is great. Plus we had a cool flight simulator station for people to practice flying (and crashing).
Day 2 was a field day - we spent most of the day at the Davis hobbycraft airfield where we practiced taking off, landing, mission planning, and emergency maneuvers. We had an excellent lunch provided by the Street Cravings food truck. What a day! It was hot hot hot, but there was lots of shade, and a nice breeze. Anyway, we had a great day, with everyone getting their hands on the commands. Our Esri rep Mark Romero gave us a demo on Esri's Drone2Map software, and some of the lidar functionality in ArcGIS Pro.
Day 3 focused on data analysis. We had three workshops ready for the group to chose from, from forestry, agriculture, and rangelands. Prior to the workshops we had great talks from Jacob Flanagan and GreenValley Intl, and Ali Pourreza from Kearney Research and Extension Center. Ali is developing a drone-imagery-based database of the individual trees and vines at Kearney - he calls it the "Virtual Orchard". Jacob talked about the overall mission of GVI and how the company is moving into more comprehensive field and drone-based lidar mapping and software. Angad Singh from Pix4D gave us a master class in mapping from drones, covering georeferencing, the Pix4D workflow, and some of the checks produced for you a the end of processing.
One of our key goals of the DroneCamp was to jump start our California Drone Ecosystem concept. I talk about this in my CalAg Editorial. We are still in the early days of this emerging field, and we can learn a lot from each other as we develop best practices for workflows, platforms and sensors, software, outreach, etc. Our research and decision-making teams have become larger, more distributed, and multi-disciplinary; with experts and citizens working together, and these kinds of collaboratives are increasingly important. We need to collaborate on data collection, storage, & sharing; innovation, analysis, and solutions. If any of you out there want to join us in our California drone ecosystem, drop me a line.
Thanks to ANR for hosting us, thanks to the wonderful participants, and thanks especially to our sponsors (GreenValley Intl, Esri, and Pix4D). Specifically, thanks for:
Recently, Esri has been holding an Imagery and Mapping Forum prior to the main User Conference. This year I was able to join as an invited panelist for the Executive Panel and Closing Remarks session on Sunday. During the day I hung out in the Imaging and Innovation Zone, in front of the Drone Zone (gotta get one of these for ANR). This was well worth attending: smaller conference - focused topics - lots of tech reveals - great networking.
Notes from the day: Saw demos from a range of vendors, including:
We wrapped up the day with a panel discussion, moderated by Esri's Kurt Schwoppe, and including Lawrie Jordan from Esri, Greg Koeln from MDA, Dustin Gard-Weiss from NGA, Amy Minnick from DigitalGlobe, Hobie Perry from USFS-FIA, David Day from PASCO, and me. We talked about the promise and barriers associated with remote sensing and image processing from all of our perspectives. I talked alot about ANR and IGIS and the use of geospatial data, analysis and viz for our work in ANR. Some fun things that came out of the panel discussion were:
Notes and stray thoughts:
Good fun! Now more from Shane and Robert at the week-long Esri UC!
UC ANR was a sponsor for the FOODIT: Fork to Farm meeting in June 2017: http://mixingbowlhub.com/events/food-fork-farm/. Many of us were there to learn about what was happening in the food-data-tech space and learn how UCANR can be of service. It was pretty cool. First, it was held in the Computer History Museum, which is rad. Second, the idea of the day was to link partners, industry, scientists, funders, and foodies, around sustainable food production, distribution, and delivery. Third, there were some rad snacks (pic below).
We had an initial talk from Mikiel Bakker from Google Food, who have broadened their thinking about food to include not just feeding Googlers, but also the overall food chain and food system sustainability. They have developed 5 "foodshots" (i.e. like "moonshot" thinking): 1) enable individuals to make better choices, 2) shift diets, 3) food system transparency, 4) reduce food losses, and 5) how to make a closed, circular food system.
We then had a series of moderated panels.
The Dean's List introduced a panel of University Deans, moderated by our very own Glenda Humiston @UCANR, and included Helene Dillard (UCDavis), Andy Thulin (CalPoly), Wendy Wintersteen (Iowa State). Key discussion points included lack of food system transparency, science communication and literacy, making money with organics, education and training, farm sustainability and efficiency, market segmentation (e.g. organics), downstream processing, and consumer power to change food systems. Plus the Amazon purchase of Whole Foods.
The Tech-Enabled Consumer session featured 4 speakers from companies who feature tech around food. Katie Finnegan from Walmart, David McIntyre from Airbnb, Barbara Shpizner from Mattson, Michael Wolf from The Spoon. Pretty neat discussion around the way these diverse companies use tech to customize customer experience, provide cost savings, source food, contribute to a better food system. 40% of food waste is in homes, another 40% is in the consumer arena. So much to be done!
The session on Downstream Impacts for the Food Production System featured Chris Chochran from ReFed @refed_nowaste, Sabrina Mutukisna from The Town Kitchen @TheTownKitchen, Kevin Sanchez from the Yolo Food Bank @YoloFoodBank, and Justin Siegel from UC Davis International Innovation and Health. We talked about nutrition for all, schemes for minimizing food waste, waste streams, food banks, distribution of produce and protein to those who need them (@refed_nowaste and @YoloFoodBank), creating high quality jobs for young people of color in the food business (@TheTownKitchen), the amount of energy that is involved in the food system (David Lee from ARPA-E); this means 7% of our energy use in the US inadvertently goes to CREATING FOOD WASTE. Yikes!
The session on Upstream Production Impacts from New Consumer Food Choices featured Ally DeArman from Food Craft Institute @FoodCraftInst, Micke Macrie from Land O' Lakes, Nolan Paul from Driscoll's @driscollsberry, and Kenneth Zuckerberg from Rabobank @Rabobank. This session got cut a bit short, but it was pretty interesting. Especially the Food Craft Institute, whose mission is to help "the small guys" succeed in the food space.
The afternoon sessions included some pitch competitions, deep dive breakouts and networking sessions. What a great day for ANR.
So much to learn! Here is my distillation of the main take-homes from last week.
Day 4 http://neondataskills.org/data-institute-17/day4/
This is it! Final day of LUV-DATA. Today we focused on hyperspectral data and vegetation. Paul Gader from the University of Florida kicked off the day with a survey of some of his projects in hyperspectral data, explorations in NEON data, and big data algorithmic challenges. Katie Jones talked about the terrestrial observational plot protocol at the NEON sites. Sites are either tower (in tower air-shed) or distributed (throughout site). She focused on the vegetation sampling protocols (individual, diversity, phenology, biomass, productivity, biogeochemistry). Data to be released in the fall. Samantha Weintraub talked to us about foliar chemistry data (e.g. C, N, lignin, chlorophyll, trace elements) and linking with remote sensing. Since we are still learning about fundamental controls on canopy traits within and between ecosystems, and we have a poor understanding of their response to global change, this kind of NEON work is very important. All these foliar chemistry data will be released in the fall. She also mentioned the extensive soil biogeochemical and microbial measurements in soil plots (30cm depth) again in tower and distributed plots (during peak greenness and 2 seasonal transitions).
The coding work focused on classifying spectra (Classification of Hyperspectral Data with Ordinary Least Squares in Python), (Classification of Hyperspectral Data with Principal Components Analysis in Python) and (Using SciKit for Support Vector Machine (SVM) Classification with Python), using our new best friend Jupyter Notebooks. We spent most of the time talking about statistical learning, machine learning and the hazards of using these without understanding of the target system.
Fun additional take-home messages/resources:
Thanks to everyone today! Megan Jones (ran a flawless workshop), Paul Gader (remote sensing use cases/classification), Katie Jones (NEON terrestrial vegetation sampling), Samantha Weintraub (foliar chemistry data).
And thanks to NEON for putting on this excellent workshop. I learned a ton, met great people, got re-energized about reproducible workflows (have some ideas about incorporating these concepts into everyday work), and got to spend some nostalgic time walking around my former haunts in Boulder.
Today we focused on uncertainty. Yay! http://neondataskills.org/data-institute-17/day3/
Tristan Goulden gave a talk on sources of uncertainty in the discrete return lidar data. Uncertainty comes from two main sources: geolocation - horizontal and vertical (e.g. distance from base station, distribution and number of satellites, and accuracy of IMU), and processing (e.g. classification of point cloud, interpolation method ). The NEON remote sensing team has developed tests for each of these error sources. NEON provides with all their lidar data a simulated point cloud error product, with horizontal and vertical error per point in LAS format (cool!). These products show the error is largest at the edges of scans, obvi.
We then coded an example from the PRIN NEON site, where NEON captured lidar data twice within 2 days, and so we could explore how different the data were. Again, we used Jupyter Notebooks and explored the relative differences in DSM and DTM values between the two lidar captures. The differences are random, but non-negligible, at least for DSM. For the DTM, the range = 0.0-20cm; but for the DSM the range = 0.0-1.5. The mean DSM is 6.34m, so the difference can be ~20%. The take home is that despite a 15cm accuracy spec from vendors on vertical accuracies, you can get very different measures on different flights and those can be considerable, especially with vegetation. In fact, NEON meets its 15cm accuracy requirements only in non-vegetated areas. Note, when you download NEON data, you can get line-to-line differences in the NEON lidar metadata, to kind of explore this. But assume if you are in heavily vegetated areas you should expect higher than 15cm error.
After lunch we launched into the NEON Imaging Spectrometer data and uncertainty with Nathan This is something I had not really thought about before this workshop.
We talked about orthorectfication and geolocation, focal plan characterization, spectral calibration and radiometric calibration and all the possible sources of error that can creep into the data, like blurring and ghosting of light. NEON calibrates their data across these areas, and provided information on each. I don't think there are many standards for reporting these kinds of spectral uncertainties.
The first live coding exercise (Hyperspectral Variation Uncertainty Analysis in Python) looked at the NEON site F07A, at which NEON acquired 18 individual flights (for BRDF work) over an hour on one day. We used these data and plotted the different spectral reflectance curves for several pixels. For a vegetated pixel, the NIR can vary tremendously! (e.g. 20% reflectance compared to 50% reflectance, depending on time of day, solar angle, etc.) Wow! I should note that the related indices - NDVI, which are ratios, will not be as affected. Also, you can normalize the output using some nifty methods like the Standard Normal Variate (SNV) algorithm, if you have large areas over which you can gather multiple samples.
The second live coding exercise (Assessing Spectrometer Accuracy using Validation Tarps with Python) focused on a calibration experiment they conducted at CHEQ for the NIS instrument. They laid out two reflectance tarps - 3% (black) and 48% (white), measured reflectance with an ASD spectrometer, and flew over with the NIS. We compared the data across wavelengths. Results summary: small differences between ASD and NIS across wavelengths; water absorption bands play a role; % differences can be quite high - up to 50% for the black tarp. This is mostly from stray light from neighboring areas. NEON has a calibration method for this (they call it their "de-blurring correction").
Fun additional take-home messages/resources:
Thanks to everyone today! Megan Jones (our fearless leader), Tristan Goulden (Discrete Lidar Uncertainty and all the coding), Nathan Leisso (spectral data uncertainty), and Amanda Roberts (NEON intern - spectral uncertainty).
First of all, Pearl Street Mall is just as lovely as I remember, but OMG it is so crowded, with so many new stores and chains. Still, good food, good views, hot weather, lovely walk.
Welcome to Day 2! http://neondataskills.org/data-institute-17/day2/
Our morning session focused on reproducibility and workflows with the great Naupaka Zimmerman. Remember the characteristics of reproducibility - organization, automation, documentation, and dissemination. We focused on organization, and spent an enjoyable hour sorting through an example messy directory of misc data files and code. The directory looked a bit like many of my directories. Lesson learned. We then moved to working with new data and git to reinforce yesterday's lessons. Git was super confusing to me 2 weeks ago, but now I think I love it. We also went back and forth between Jupyter and python stand alone scripts, and abstracted variables, and lo and behold I got my script to run. All the git stuff is from http://swcarpentry.github.io/git-novice/
The afternoon focused on Lidar (yay!) and prior to coding we talked about discrete and waveform data and collection, and the opentopography (http://www.opentopography.org/) project with Benjamin Gross. The opentopography talk was really interesting. They are not just a data distributor any more, they also provide a HPC framework (mostly TauDEM for now) on their servers at SDSC (http://www.sdsc.edu/). They are going to roll out a user-initiated HPC functionality soon, so stay tuned for their new "pluggable assets" program. This is well worth checking into. We also spent some time live coding with Python with Bridget Hass working with a CHM from the SERC site in California, and had a nerve-wracking code challenge to wrap up the day.
Fun additional take-home messages/resources:
Thanks to everyone today! Megan Jones (our fearless leader), Naupaka Zimmerman (Reproducibility), Tristan Goulden (Discrete Lidar), Keith Krause (Waveform Lidar), Benjamin Gross (OpenTopography), Bridget Hass (coding lidar products).
I left Boulder 20 years ago on a wing and a prayer with a PhD in hand, overwhelmed with bittersweet emotions. I was sad to leave such a beautiful city, nervous about what was to come, but excited to start something new in North Carolina. My future was uncertain, and as I took off from DIA that final time I basically had Tom Petty's Free Fallin' and Learning to Fly on repeat on my walkman. Now I am back, and summer in Boulder is just as breathtaking as I remember it: clear blue skies, the stunning flatirons making a play at outshining the snow-dusted Rockies behind them, and crisp fragrant mountain breezes acting as my Madeleine. I'm back to visit the National Ecological Observatory Network (NEON) headquarters and attend their 2017 Data Institute, and re-invest in my skillset for open reproducible workflows in remote sensing.
Day 1 Wrap Up from the NEON Data Institute 2017
What a day! http://neondataskills.org/data-institute-17/day1/
Attendees (about 30) included graduate students, old dogs (new tricks!) like me, and research scientists interested in developing reproducible workflows into their work. We are a pretty even mix of ages and genders. The morning session focused on learning about the NEON program (http://www.neonscience.org/): its purpose, sites, sensors, data, and protocols. NEON, funded by NSF and managed by Battelle, was conceived in 2004 and will go online for a 30-year mission providing free and open data on the drivers of and responses to ecological change starting in Jan 2018. NEON data comes from IS (instrumented systems), OS (observation systems), and RS (remote sensing). We focused on the Airborne Observation Platform (AOP) which uses 2, soon to be 3 aircraft, each with a payload of a hyperspectral sensor (from JPL, 426, 5nm bands (380-2510 nm), 1 mRad IFOV, 1 m res at 1000m AGL) and lidar (Optech and soon to be Riegl, discrete and waveform) sensors and a RGB camera (PhaseOne D8900). These sensors produce co-registered raw data, are processed at NEON headquarters into various levels of data products. Flights are planned to cover each NEON site once, timed to capture 90% or higher peak greenness, which is pretty complicated when distance and weather are taken into account. Pilots and techs are on the road and in the air from March through October collecting these data. Data is processed at headquarters.
In the afternoon session, we got through a fairly immersive dunk into Jupyter notebooks for exploring hyperspectral imagery in HDF5 format. We did exploration, band stacking, widgets, and vegetation indices. We closed with a fast discussion about TGF (The Git Flow): the way to store, share, control versions of your data and code to ensure reproducibility. We forked, cloned, committed, pushed, and pulled. Not much more to write about, but the whole day was awesome!
Fun additional take-home messages:
Thanks to everyone today, including: Megan Jones (Main leader), Nathan Leisso (AOP), Bill Gallery (RGB camera), Ted Haberman (HDF5 format), David Hulslander (AOP), Claire Lunch (Data), Cove Sturtevant (Towers), Tristan Goulden (Hyperspectral), Bridget Hass (HDF5), Paul Gader, Naupaka Zimmerman (GitHub flow).
Just trying to get my head around some of the new big raster processors out there, in addition of course to Google Earth Engine. Bear with me while I sort through these. Thanks for raster sleuth Stefania Di Tomasso for helping.
1. Geotrellis (https://geotrellis.io/)
Geotrellis is a Scala-based raster processing engine, and it is one of the first geospatial libraries on Spark. Geotrellis is able to process big datasets. Users can interact with geospatial data and see results in real time in an interactive web application (for regional, statewide dataset). For larger raster datasets (eg. US NED). GeoTrellis performs fast batch processing using Akka clustering to distribute data across the cluster. GeoTrellis was designed to solve three core problems, with a focus on raster processing:
2. GeoPySpark - in synthesis GeoTrellis for Python community
Geopyspark provides python bindings for working with geospatial data on PySpark (PySpark is the Python API for Spark). Spark is open source processing engine originally developed at UC Berkeley in 2009. GeoPySpark makes Geotrellis (https://geotrellis.io/) accessible to the python community. Scala is a difficult language so they have created this Python library.
3. RasterFoundry (https://www.rasterfoundry.com/)
They say: "We help you find, combine and analyze earth imagery at any scale, and share it on the web." And "Whether you’re working with data already accessible through our platform or uploading your own, we do the heavy lifting to make processing your imagery go quickly no matter the scale."
Key RasterFoundry workflow:
From the Kitware blog: Kitware has partnered with The NASA Earth Exchange (NEX) to design GeoNotebook, a Jupyter Notebook extension created to solve these problems (i.e. big raster data stacks from imagery). Their shared vision: a flexible, reproducible analysis process that makes data easy to explore with statistical and analytics services, allowing users to focus more on the science by improving their ability to interactively assess data quality at scale at any stage of the processing.
Extending Jupyter Notebooks and Jupyter Hub, this python analysis environment provides the means to easily perform reproducible geospatial analysis tasks that can be saved at any state and easily shared. As the geospatial datasets come in, they are ingested into the system and converted into tiles for visualization, creating a dynamic map that can be managed from the web UI and can communicate back to a server to perform operations like data subsetting and visualization.
Today we had our 1st Data Science for the 21st Century Program Conference. Some cool things that I learned:
Plus plus, Carl B shared Drew Conway's DS fig, which I understand is making the DS rounds:
Are you a college student, researcher or professor? We’re looking for innovative academics, researchers and scientists to unlock the power of a one-of-a-kind dataset. You can now apply for access to Planet’s unique dataset for non-commercial research purposes. In an area as large as 2,000 square kilometers, you’ll have access to download imagery, analyze trends, and publish your results.
Day 3: I opened the day with a lovely swim with Elizabeth Havice (in the largest pool in New England? Boston? The Sheraton?) and then embarked on a multi-mile walk around the fair city of Boston. The sun was out and the wind was up, showing the historical buildings and waterfront to great advantage. The 10-year old Institute of Contemporary Art was showing in a constrained space, but it did host an incredibly moving video installation from Steve McQueen (Director of 12 Years a Slave) called “Ashes” about the life and death of a young fisherman in Grenada.
My final AAG attendance involved two plenaries hosted by the Remote Sensing Specialty Group and the GIS Specialty Group, who in their wisdom, decided to host plenaries by two absolute legends in our field – Art Getis and John Jensen – at the same time. #battleofthetitans. #gisvsremotesensing. So, I tried to get what I could from both talks. I started with the Waldo Tobler Lecture given by Art Getis: The Big Data Trap: GIS and Spatial Analysis. Compelling title! His perspective as a spatial statistician on the big data phenomena is a useful one. He talks about how data are growing fast: Every minute – 98K tweets; 700K FB updates; 700K Google searches; 168+M emails sent; 1,820 TB of data created. Big data is growing in spatial work; new analytical tools are being developed, data sets are generated, and repositories are growing and becoming more numerous. But, there is a trap. And here is it. The trap of Big Data:
10 Erroneous assumptions to be wary of:
He then asked: what is the role of spatial scientists in the big data revolution? He says our role is to find relationships in a spatial setting; to develop technologies or methods; to create models and use simulation experiments; to develop hypotheses; to develop visualizations and to connect theory to process.
The summary from his talk is this: Start with a question; Differentiate excitement from usefulness; Appropriate scale is mandatory; and Remember more may or may not be better.
When Dr Getis finished I made a quick run down the hall to hear the end of the living legend John Jensen’s talk on drones. This man literally wrote the book(s) on remote sensing, and he is the consummate teacher – always eager to teach and extend his excitement to a crowded room of learners. His talk was entitled Personal and Commercial Unmanned Aerial Systems (UAS) Remote Sensing and their Significance for Geographic Research. He presented a practicum about UAV hardware, software, cameras, applications, and regulations. His excitement about the subject was obvious, and at parts of his talk he did a call and response with the crowd. I came in as he was beginning his discussion on cameras, and he also discussed practical experience with flight planning, data capture, and highlighted the importance of obstacle avoidance and videography in the future. Interestingly, he has added movement to his “elements of image interpretation”. Neat. He says drones are going to be routinely part of everyday geographic field research.
What a great conference, and I feel honored to have been part of it.
Day 1: Thursday I focused on the organized sessions on uncertainty and context in geographical data and analysis. I’ve found AAGs to be more rewarding if you focus on a theme, rather than jump from session to session. But less steps on the iWatch of course. There are nearly 30 (!) sessions of speakers who were presenting on these topics throughout the conference.
An excellent plenary session on New Developments and Perspectives on Context and Uncertainty started us off, with Mei Po Kwan and Michael Goodchild providing overviews. We need to create reliable geographical knowledge in the face of the challenges brought up by uncertainty and context, for example: people and animals move through space, phenomena are multi-scaled in space and time, data is heterogeneous, making our creation of knowledge difficult. There were sessions focusing on sampling, modeling, & patterns, on remote sensing (mine), on planning and sea level rise, on health research, on urban context and mobility, and on big data, data context, data fusion, and visualization of uncertainty. What a day! All of this is necessarily interdisciplinary. Here are some quick insights from the keynotes.
Mei Po Kwan focused on uncertainty and context in space and time:
As expected, Michael Goodchild gave a master class in context and uncertainty. No one else can deliver such complex material so clearly, with a mix of theory and common sense. Inspiring. Anyway, he talked about:
My session went well. I chaired a session on uncertainty and context in remote sensing with 4 great talks from Devin White and Dave Kelbe from Oak Ridge NL who did a pair of talks on ORNL work in photogrammetry and stereo imagery, Corrine Coakley from Kent State who is working on reconstructing ancient river terraces, and Chris Amante from the great CU who is developing uncertainty-embedded bathy-topo products. My talk was on uncertainty in lidar inputs to fire models, and I got a great question from Mark Fonstad about the real independence of errors – as in canopy height and canopy base height are likely correlated, so aren’t their errors? Why do you treat them as independent? Which kind of blew my mind, but Qinghua Guo stepped in with some helpful words about the difficulties of sampling from a joint probability distribution in Monte Carlo simulations, etc.
Plus we had some great times with Jacob, Leo, Yanjun and the Green Valley International crew who were showcasing their series of Lidar instruments and software. Good times for all!
Our third GIF Spatial Data Science Bootcamp has wrapped! We had an excellent 3 days with wonderful people from a range of locations and professions and learned about open tools for managing, analyzing and visualizing spatial data. This year's bootcamp was sponsored by IGIS and GreenValley Intl (a Lidar and drone company). GreenValley showcased their new lidar backpack, and we took an excellent shot of the bootcamp participants. What is Paparazzi in lidar-speak? Lidarazzi?
Here is our spin: We live in a world where the importance and availability of spatial data are ever increasing. Today’s marketplace needs trained spatial data analysts who can:
At the Spatial Data Science Bootcamp we learn how to integrate modern Spatial Data Science techniques into your workflow through hands-on exercises that leverage today's latest open source and cloud/web-based technologies.
Hi all! I was recently profiled for the excellent website: Women in GIS (or WiGIS). This is a group of technical-minded women who maintain this website to feature women working in the geospatial industry with our Who We Are spotlight series. and in addition, the individuals in this group make their presence known at conferences like CalGIS and ESRI’s UCs. We also plan to host a number of online resources women might find useful to start or navigate their GIS career.
Excellent time, and thanks for the opportunity!
IGIS is pleased to announce a three-day "Dronecamp" to be held July 25-27, 2017, in Davis. This bootcamp style workshop will provide "A to Z" training in using drones for research and resource management, including photogrammetry and remote sensing, safety and regulations, mission planning, flight operations (including 1/2 day of hands-on practice), data processing, analysis, and visualization. The workshop content will help participants prepare for the FAA Part 107 Remote Pilot exam. Participants will also hear about the latest technology and trends from researchers and industry representatives.
Dronecamp builds upon a series of workshops that have been developed by IGIS and Sean Hogan starting in 2016. Through these workshops and our experiences with drone research, we've learned that the ability to use mid-range drones as scientifically robust data collection platforms requires a proficiency in a diverse set of skills and knowledge that exceeds what can be covered in a traditional workshop. Dronecamp aims to cover all the bases, helping participants make a great leap forward in their own drone programs.
Dronecamp is open to all but will have a focus on applications in agriculture and natural resources. No experience is necessary. We expect interest to exceed the number of seats, so all interested participants must fill in an application before they can register. Applications are due on April 15, 2017. For further information, please visit http://igis.ucanr.edu/dronecamp/. Dronecamp Flier.
Christine Wilkinson reports here on her trip to East Africa as part of her dissertation research. She explored potential field sites, networked with communities and wildlife managers, and brainstormed my dissertation research questions. And took some amazing pics. http://africa.berkeley.edu/situated-brainstorming