Bibliographic record
Abstract
The TreeD © software, version 0.8, is an application that loads an aerial or satellite image and detects trees by template matching. The software is developed at the Remote Sensing Laboratory, Swedish University of Agricultural Sciences ( SLU) in Umea, Sweden. This is beta software and the methods and algorithms are continuously being improved upon. Please report any errors you find in the software to the Remote Sensing Laboratory. The algorithms for tree detection used in the application are based on a PhD thesis by Richard J ames Pollock (1996), University of British Columbia, Canada. The application is build upon two software libraries, Intel® Image Processing Library, IPL ©and wxWindows ©. The I PL is used for image processing and wx Windows is used as a graphical user interface. The report contains three major sections, a manual, a method description and a software description. The manual is for someone, without any prior knowledge of template matching, who wants to run the software. The method and software descriptions are for someone that wants to build a similar application as Tree D or as a support for in-house development at the Remote Sensing Laboratory. The TreeD 0.8 application is available at the Remote Sensing Laboratory, SLU and is primarily used as a research tool for single tree detection. The software runs on a Microsoft® Windows 2 000 workstation. The application binary depends on the Intel® Image Processing Library, IPL © DLLs and consequently they need to be put into the same folder as the program. All of these binaries can be found at SLU. The input to the application is an aerial/satellite image (central or orthogonal projection), a tree library, information about the camera and solar positions , and a path to a directory to put the results in. The current status of the input variables can be viewed and changed before starting the correlation of the image. The output from the application consists of three text files , status. txt, treelist.txt and probable_treelist.txt. I f there are old files with these names at the result directory they will be overwritten. To save a new batch you can either rename the oldfiles or use a new result directory. ·· The application assumes that the terrain is fairly flat and that the camera is positioned approximately in a nadir view. I f these conditions are not fulfilled the accuracy of the positioning and detection of the trees will decrease.
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 | 0.008 |
| 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 | 0.005 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.260 | 0.277 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".