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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe Satellite Geo QCM leaderboard, as described in a September 22 article by RESK, tests whether a model can identify a location in an aerial or satellite image from four choices. It uses fixed questions and options, marks each answer right or wrong, and reports overall and difficulty-level accuracy. The published scores are an article-reported snapshot, not verified live leaderboard values.
What does a Satellite Geo QCM question look like?
Each item pairs an aerial or satellite image with one question asking the model to choose the correct location from four options. RESK’s description says every model receives the same wording and choices, with no hint, retry, or alternate framing.
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The format is deliberately constrained: the output is a selection among candidates, rather than a free-form explanation of an image. That makes the task and its answer key easier to compare across models within this setup.
How are answers scored?
RESK describes scoring as deterministic: the selected option is compared with the correct answer and marked right or wrong. There is no LLM judge evaluating a written response. The resulting accuracy is the percentage of questions answered correctly, as characterized in the article.
#1 Best Overall
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Fixed inputs and mechanical scoring remove some sources of variation associated with open-ended prompts and model-based judging. They do not, by themselves, establish that dataset construction, geographic coverage, or submission controls are unbiased or independently audited.
What does the leaderboard report?
The article says results are shown as overall accuracy and accuracy by difficulty, with a public verbatim response trail where readers can inspect the image, answer choices, and model response. Its table reports these figures:
Rank #2
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| Model | Reported accuracy | Context |
|---|---|---|
| DeepSeek V4 Flash Vision (exp) | 98.18 | Figure in RESK’s article leaderboard table; the article does not establish that it is a current live value. |
| GLM 5.2 | 78.0 | Figure in the same table; RESK identifies this as the author’s own submission. The article does not establish that it is a current live value. |
These are the values reported in the article, not independently verified current standings. The opened page did not print a publication year alongside September 22, and the available material does not establish the live leaderboard’s status.
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How should you interpret a high score?
A high score indicates success on this specific four-choice image-location task. It does not establish general reasoning, instruction-following ability, or performance on open-ended vision tasks; RESK explicitly distinguishes those capabilities from what the benchmark measures. A four-option test also permits guessing.
Rank #3
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For a more useful comparison than the headline number alone, examine:
- Difficulty-level accuracy: check whether performance holds across the reported difficulty categories, rather than relying only on the aggregate.
- The response trail: inspect individual images, choices, and answers. The article does not publish a formal error taxonomy, but the trail may help readers notice whether a miss appears to involve reading the options or recognizing the location.
- Task fit: decide whether selecting from four locations in satellite imagery resembles the vision work you intend to evaluate. Results should not be generalized to unrelated image tasks.
- Snapshot and submission context: treat table values as the article’s reported snapshot, and note that one listed result is identified as the author’s own submission.
What details are not established?
The available Satellite Geo QCM description does not establish the dataset’s size or image provenance, geographic balance, exact difficulty rubric, train/test or contamination controls, or the controls applied to submissions. Without those details, readers cannot independently assess how representative the questions are or how robust the leaderboard is to data or submission issues.
Rank #4
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How is this different from other geospatial benchmarks?
The Cloud-Based Geospatial Benchmark (CBGB) is a separate, more code-oriented evaluation—not another name for Satellite Geo QCM. Its 2025 paper describes 45 expert-curated Earth-observation scenarios in which LLM agents generate code to produce short numerical answers. The study compares runs with and without feedback from the execution environment for error correction. It reports that feedback generally helped and that the highest performance was 71%; reasoning variants outperformed non-thinking variants in that study. Those findings concern CBGB and cannot be used to interpret Satellite Geo QCM scores.
CBGB’s authors describe its questions and reference solutions as curated by experts with Earth-observation familiarity and programming expertise. Its code-generation workflow measures a different combination of skills from choosing a location among four image-based options.
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