Bibliographic record
Abstract
The TreeD © software, \nversion 0.8, \nis an application that loads an aerial or satellite image and \ndetects trees by template matching. The software is developed at the Remote Sensing Laboratory, \nSwedish University of Agricultural Sciences ( SLU) \nin Umea, Sweden. This is beta software and \nthe methods and algorithms are continuously being improved upon. Please report any errors you \nfind in the software to the Remote Sensing Laboratory. \nThe algorithms for tree detection used in the application are based on a PhD thesis by Richard \nJ \names Pollock (1996), \nUniversity of British Columbia, Canada. The application is build upon two \nsoftware libraries, Intel® Image Processing Library, IPL ©and wxWindows ©. The I PL is \nused for image processing and wx \nWindows is used as a graphical user interface. \nThe report contains three major sections, \na manual, \na method description and a software \ndescription. The manual is for someone, \nwithout any prior knowledge of template matching, \nwho \nwants to run the software. The method and software descriptions are for someone that wants to \nbuild a similar application as Tree D or as a support for in-house development at the Remote \nSensing Laboratory. \n \nThe TreeD 0.8 application is available at the Remote Sensing Laboratory, SLU and is primarily \nused as a research tool for single tree detection. The software runs on a Microsoft® Windows \n2 000 workstation. The application \nbinary depends on the Intel® Image Processing Library, \nIPL © DLLs and consequently they need to be put into the same folder as the program. All of \nthese binaries can be found at SLU. \nThe input to the application is an aerial/satellite image (central or orthogonal projection), \na tree \nlibrary, \ninformation about the camera and solar positions , \nand a path to a directory to put the \nresults in. The current status of the input variables can be viewed and changed before starting the \ncorrelation of the image. The output from the application consists of three text files , status. txt, \ntreelist.txt \nand probable_treelist.txt. I \nf there are old files with these names at the result \ndirectory \nthey \nwill be overwritten. To save a \nnew batch you can either rename the oldfiles or use \na new result directory. \n·· \nThe application assumes that the terrain is fairly flat and that the camera is positioned \napproximately in a nadir view. I \nf these conditions are not fulfilled the accuracy of the positioning \nand detection of the trees will decrease. \n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.421 | 0.455 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".