ASPRS 2012, held in Sacramento California, had about 1,100 participants. I am back to being bullish about our organization, as I now recognize that ASPRS is the only place in geospatial sciences where members of government, industry, and academia can meet, discuss, and network in a meaningful way. I saw a number of great talks, met with some energetic and informative industry reps, and got to catch up with old friends. Some highlights: Wednesday's Keynote speaker was David Thau from Google Earth Engine whose talk "Terapixels for Everyone" was designed to showcase the ways in which the public's awareness of imagery, and their ability to interact with geospatial data, are increasing. He calls this phenomena (and GEE plays a big role here): "geo-literacy for all", and discussed new technologies for data/imagery acquisition, processing, and dissemination to a broad public(s) that can include policy makers, land managers, and scientists. USGS's Ken Hudnut was Thursday's Keynote, and he had a sobering message about California earthquakes, and the need (and use) of geospatial intelligence in disaster preparedness.
Berkeley was well represented: Kevin and Brian from the GIF gave a great workshop on open source web, Kevin presented new developments in cal-adapt, Lisa and Iryna presented chapters from their respective dissertations, both relating to wetlands, and our SNAMP lidar session with Sam, Marek, and Feng (with Wenkai and Jacob from UCMerced) was just great!
So, what is in the future for remote sensing/geospatial analysis as told at ASPRS 2012? Here are some highlights:
- Cloud computing, massive datasets, data/imagery fusion are everywhere, but principles in basic photogrammetry should still comes into play;
- We saw neat examples of scientific visualization, including smooth rendering across scales, fast transformations, and immersive web;
- Evolving, scaleable algorithms for regional or global classification and/or change detection; for real-time results rendering with interactive (on-the-fly) algorithm parameter adjustment; and often involving open source, machine learning;
- Geospatial data and analysis are heavily, but inconsistently, deployed throughout the US for disaster response;
- Landsat 8 goes up in January (party anyone?) and USGS/NASA are looking for other novel parterships to extend the Landsat lifespan beyond that;
- Lidar is still big: with new deployable and cheaper sensors like FLASH lidar on the one hand, and increasing point density on the other;
- Obia, obia, obia! We organized a nice series of obia talks, and saw some great presentations on accuracy, lidar+optical fusion, object movements; but thorny issues about segmentation accuracy and object ontology remain;
- Public interaction with imagery and data are critical. The Public can be a broader scientific community, or a an informed and engaged community who can presumably use these types of data to support public policy engagement, disaster preparedness and response.