Syrotuck's main study is his 1976, with N=242. But he gives much more detail about distance travelled in his 1975 paper, breaking distance down every 0.2 miles. Unfortunately, he only reports probabilities, not numbers, and doesn't even report total N. We know he got more data between 1975 and 1976, but didn't know how much. Is the 1975 breakdown representative of the 1976 data? Unfortunately, no one has Syrotuck's original data. But we re-created it. (Spreadsheets available!)
Automated Image Segmentation
How to turn a USGS map or satellite photos into vector data? A couple of links, and some questions.
Back when Adam Golding and I prototyped Probability Mapper we already had an algorithm that could give the probability based on distance, terrain, vegetation, and other factors. But we were working with raster images, so we only had distance. If you had vector layers for terrain & vegetation, you'd be set.
~Originally 9 June 2008
ESRI SAR Articles
Wilderness SAR
Don Ferguson's paper shows how they used ESRI GIS software on an actual search. Obviously he's developed some SAR-specific layers. Imagine if the probability map could be auto-generated...
Yes, I'm getting in touch with him.
Australian SAR data
We have been collecting data on land SAR incidents in Australia since 2000. In November 2003 we wrote a draft report that was presented at the NATSAR council but not generally released. It was styled after the U.K. report. In June 2006 we released the final version (see below), which has evolved its own style.
Our data was collected using the form available below (or an earlier version thereof). The form itself helps define what is meant by each term or category, and is essential to interpreting the data. We have also prepared a definition key in the report, to explain our terms more precisely. The UK report gave us the idea, but there are some differences in definition.
Continue reading "Australian SAR data"
Comments on O'Connor 2004
Comments on the 2004 draft of "Controversial Topics in Inland SAR Planning" by Dan O'Connor. Written in 2004, updated in 2007.
Preface
This open letter was written in 2004. At the time, some people found it useful. In 2007, I updated it as a blog post. It was lost in the ISP crash of Oct. 2011. In Feb. 2013, I exhumed it and posted to this WordPress site. I finally fixed the formatting in Aug. 2014. As of Feb. 2013, the Internet Archive still had the original 2004 post and the updated 2007 post. In my 2007 update, I wrote this preface:
Things have moved on a bit since then [2004]. In the summer of 2006, Rick Toman and Dan O'Connor organized sweep width experiments in the Berkshires, and provided a "Camp David" style setting for ISAR and MSAR folks to meet. (Unfortunately, it shamefully took me a over a year to finish analyzing that and get the spreadsheet back to Rick. I think I still haven't posted it here. [See Berkshires 2006 Sweep Width Experiment].) As I note below, there have been developments in adding MSAR ideas to new versions of standard of SAR software. But, as an exercise in blogging, here is the original post again, with minimal edits to get it displayed properly here.
ACFR & Nuit Blanche
The Australian Centre for Field Robotics is doing a lot of good work on Bayesian search theory and sensor fusion, for multi-agent robotic systems.
Igor Carron briefly describes one of their recent technical papers in this short blog post . The abstract begins: This paper describes a decentralized Bayesian approach to coordinating multiple autonomous sensor platforms searching for a single non-evading target. Unfortunately, the article has restricted access.
However, I recommend spending awhile at Igor's blog. Follow links about Jim Gray, Bayes, SAROPS.
Thanks to Bob Koester for the reference.
Rabbit on Search Management
A. A. Milne, "The Search for Small", The House at Pooh Corner
"Now," said Rabbit, "this is a Search, and I've Organized it------".
"Done what to it?" said Pooh.
"Organized it. Which means---well, it's what you do to a Search, when you don't all look in the same place at once."
Landmine probability maps
The following paper creates landmine probability maps almost precisely in the way that I have wanted to create lost person behavior probability maps. (I'll add some more notes on it later.)
SARBayes Optimal Resource Allocation Library
(This is recovered from an old history sidebar for SORAL. The code was updated in 2008 for the AGMSAR package.)
Version 2.0 release scheduled for 28 Feb 2003.
Jan. 2003: The code is now fully redocumented using dOxygen!! The public interface can be seen here. The private interface is available to developers upon request and right now is available here. In both cases the Developer's Manual is available under the "Related Pages" link in the header. Thanks to André Oboler!
Continue reading "SARBayes Optimal Resource Allocation Library"
Data Analysis Update
Early 2003: Charles Twardy plans to reanalyze the Virginia data, correcting for some problems in last year's run. In February, we will analyze the Australian data for the draft report.
Dec 2001: In preparation for the Australian data, Adam Golding analyzed the Virginia data. Cluster analysis revealed only 4 or 5 types of lost person, assuming Gaussian (bell-shaped curve) types.
Adam Golding and Luke Hope then tested several machine-learned models, Syrotuck's model, and a simple model estimated by Rik Head. There were strong differences in predictive accuracy, but negligible differences in a more meaningful score, information reward. The most recent presentation of this work was in Charles Twardy's presentation to the NASAR 2002 conference in Charlotte, NC (June 2002).