--- title: "EarthJev" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d973b9a2-651e-4bec-8aa3-966815476618" updated: "2026-11-13" --- # EarthJev > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers typed questions about satellite image patches from all 12 Sentinel-2 bands, returning a probability for each option without writing text. A Qwen3.5-0.8B fine-tune for Earth observation researchers. EarthJev is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by dBenf ## About ### What it is EarthJev is a small vision language model for Earth observation, from a team at the University of Naples Federico II. You give it a Sentinel-2 L2A patch with all 12 bands and a question with fixed options. It returns a probability for every option and never generates free text, so each answer is one of the options you declared. ### Who it suits Researchers and developers who classify satellite imagery and want fast, calibrated answers to fixed questions about a patch. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Earth observation | Remote sensing researchers | Ask land cover questions about Sentinel-2 patches | Asking which land cover class covers most of a patch | | Environmental monitoring | Analysts who track land use | Check for features such as urban fabric across many patches | Asking whether a patch contains any urban area | | Machine learning research | Developers who study efficient models | Compare its speed and accuracy with larger generative models | Timing answers per image against a generative baseline | Skip it if you need free text descriptions, or if your imagery has only the three colour bands. ### Where to use it It runs in Python with transformers and torch, from a clone of the project repository. The files download on Windows, macOS or Linux. - Python with transformers that supports Qwen3.5, and torch - The engine from the OneJev project - Sentinel-2 L2A data in the BigEarthNet style | Use case | Who | What they get back | | --- | --- | --- | | Land cover questions | Researchers | A probability for each land cover class | | Yes or no checks | Analysts | A probability for yes and for no | | Batch question answering | Developers | Several answers about one patch from one image encoding | ### How to set it up You need Python, a GPU is not named as a requirement, and the model folder. - Python with pip - transformers with Qwen3.5 support, and torch - The OneJev engine, whose install command is on the card - A clone of this repository, which holds the earthjev\_infer module 1. Install the Hugging Face hub library. 2. Download the model files. 3. Install transformers, torch and the OneJev engine. 4. Run the example from the clone of the repository. ```sh pip install -U huggingface_hub hf download dBenf/EarthJev ``` ### How to use it Load the model from its folder, pass a 12 band array and a set of typed questions, and read the probabilities. The array stacks the 12 Sentinel-2 L2A bands, from B01 up to B12, along its first axis. ```python import numpy as np from earthjev_infer import EarthJev model = EarthJev.load(".") # this folder (downloaded with huggingface_hub.snapshot_download) s2 = np.load("examples/S2A_MSIL2A_20170701T093031_N9999_R136_T35VPK_37_55.npy") # [12, 120, 120] uint16 answers = model.predict(s2, { "dominant": {"type": "choice", "instructions": "Which land-cover class covers most of this patch?", "criteria": {"artificial": "Artificial surfaces.", "agricultural": "Agricultural areas.", "forest": "Forest and seminatural areas.", "wetlands": "Wetlands.", "water": "Water bodies."}}, "urban": {"type": "noul", "instructions": "Is there any urban fabric in this patch?"}, }) print(answers["dominant"]) # a probability for every option ``` | Tool | What it does | | --- | --- | | EarthJev.load | Loads the model from the downloaded folder | | model.predict | Takes the band array and typed questions, and returns a probability per option | ### Example You pass a 12 band patch and ask which land cover class covers most of it, with five options. The model returns a probability for each of the five, such as one for forest and one for water, and no generated text. ### For newcomers and experts If you are new to this, Sentinel-2 is a satellite that images the Earth in many bands. Start with the example patch in the repository and read the probabilities for the two sample questions. If you know the field, the model reads the logits of the option letters at one position, so every answer is valid and questions on one patch share one image encoding. The first vision layer was widened from 3 to 12 channels. The authors report calibration temperatures fitted on validation questions. 12 band patch and typed question → One image encoding is shared → Option letter logits are read → A probability per option returns ### Good to know Of three training runs, the authors kept the one with the best validation accuracy, 85.63 percent over 36,698 questions. In the card's tests it was several times faster than generative models of similar accuracy. Card test on CORINE questions | Model | Accuracy percent | Milliseconds per question | | --- | --- | --- | | EarthJev | 86.0 | 19.9 | | Qwen3.5-0.8B, generative | 86.8 | 134.7 | | InternVL3-2B, LoRA | 87.1 | 84.5 | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the Sentinel-2 patches you pass in - Not for: free text answers, or imagery with only the three colour bands, since replacing the other nine bands with their training mean costs 4.5 points on CORINE ## README ````markdown # EarthJev Answers typed questions about satellite image patches from all 12 Sentinel-2 bands, returning a probability for each option without writing text. A Qwen3.5-0.8B fine-tune for Earth observation researchers. Made by dBenf on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: EarthJev is a small vision language model for Earth observation, from a team at the University of Naples Federico II. You give it a Sentinel-2 L2A patch with all 12 bands and a question with fixed options. It returns a probability for every option and never generates free text, so each answer is one of the options you declared. Who it suits: Researchers and developers who classify satellite imagery and want fast, calibrated answers to fixed questions about a patch. ```table Industry | Who uses it | How they use it | Example Earth observation | Remote sensing researchers | Ask land cover questions about Sentinel-2 patches | Asking which land cover class covers most of a patch Environmental monitoring | Analysts who track land use | Check for features such as urban fabric across many patches | Asking whether a patch contains any urban area Machine learning research | Developers who study efficient models | Compare its speed and accuracy with larger generative models | Timing answers per image against a generative baseline ``` Skip it if you need free text descriptions, or if your imagery has only the three colour bands. Where to use it: It runs in Python with transformers and torch, from a clone of the project repository. The files download on Windows, macOS or Linux. - Python with transformers that supports Qwen3.5, and torch - The engine from the OneJev project - Sentinel-2 L2A data in the BigEarthNet style ```table Use case | Who | What they get back Land cover questions | Researchers | A probability for each land cover class Yes or no checks | Analysts | A probability for yes and for no Batch question answering | Developers | Several answers about one patch from one image encoding ``` How to set it up: You need Python, a GPU is not named as a requirement, and the model folder. - Python with pip - transformers with Qwen3.5 support, and torch - The OneJev engine, whose install command is on the card - A clone of this repository, which holds the earthjev_infer module 1. Install the Hugging Face hub library. 2. Download the model files. 3. Install transformers, torch and the OneJev engine. 4. Run the example from the clone of the repository. ```sh pip install -U huggingface_hub hf download dBenf/EarthJev ``` How to use it: Load the model from its folder, pass a 12 band array and a set of typed questions, and read the probabilities. The array stacks the 12 Sentinel-2 L2A bands, from B01 up to B12, along its first axis. ```python import numpy as np from earthjev_infer import EarthJev model = EarthJev.load(".") # this folder (downloaded with huggingface_hub.snapshot_download) s2 = np.load("examples/S2A_MSIL2A_20170701T093031_N9999_R136_T35VPK_37_55.npy") # [12, 120, 120] uint16 answers = model.predict(s2, { "dominant": {"type": "choice", "instructions": "Which land-cover class covers most of this patch?", "criteria": {"artificial": "Artificial surfaces.", "agricultural": "Agricultural areas.", "forest": "Forest and seminatural areas.", "wetlands": "Wetlands.", "water": "Water bodies."}}, "urban": {"type": "noul", "instructions": "Is there any urban fabric in this patch?"}, }) print(answers["dominant"]) # a probability for every option ``` ```table Tool | What it does EarthJev.load | Loads the model from the downloaded folder model.predict | Takes the band array and typed questions, and returns a probability per option ``` Example: You pass a 12 band patch and ask which land cover class covers most of it, with five options. The model returns a probability for each of the five, such as one for forest and one for water, and no generated text. For newcomers and experts: If you are new to this, Sentinel-2 is a satellite that images the Earth in many bands. Start with the example patch in the repository and read the probabilities for the two sample questions. If you know the field, the model reads the logits of the option letters at one position, so every answer is valid and questions on one patch share one image encoding. The first vision layer was widened from 3 to 12 channels. The authors report calibration temperatures fitted on validation questions. ```flow 12 band patch and typed question One image encoding is shared Option letter logits are read A probability per option returns ``` Good to know: Of three training runs, the authors kept the one with the best validation accuracy, 85.63 percent over 36,698 questions. In the card's tests it was several times faster than generative models of similar accuracy. ```table Title: Card test on CORINE questions Model | Accuracy percent | Milliseconds per question EarthJev | 86.0 | 19.9 Qwen3.5-0.8B, generative | 86.8 | 134.7 InternVL3-2B, LoRA | 87.1 | 84.5 ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the Sentinel-2 patches you pass in - Not for: free text answers, or imagery with only the three colour bands, since replacing the other nine bands with their training mean costs 4.5 points on CORINE ## On Hugging Face, 14 November 2026 - Task: image text to text - Library: transformers - Parameters: 1.1B - Weights: about 2.2 GB in BF16; about 555 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 159 - Likes: 2 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3.5-0.8B - Tags: transformers, safetensors, qwen3_5, image-text-to-text, remote-sensing, sentinel-2, multispectral, earth-observation, visual-question-answering, calibration, decision-model, system-one ## Links - Model: https://huggingface.co/dBenf/EarthJev - Files: https://huggingface.co/dBenf/EarthJev/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download dBenf/EarthJev ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [dBenf/EarthJev on Hugging Face](https://huggingface.co/dBenf/EarthJev) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | EarthJev (this page) | 159 | 2 | 1.1B | apache-2.0 | 2026-11-03 | | [MANTA-27B](https://mindket.com/p/407608cf-6b3c-48f6-8e9d-74c777d9ad28) | 46 | 4 | 27.5B | apache-2.0 | 2026-11-10 | | [Qwen-Drive-1.0-4B](https://mindket.com/p/34473e4a-e107-4885-9b19-147c5b85e1ac) | 7,740 | 259 | 4.5B | apache-2.0 | 2026-09-02 | | [Qwen-Image-Bench](https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e) | 20,875 | 116 | 27.4B | apache-2.0 | 2026-05-28 | | [NV-Reason-CT](https://mindket.com/p/f1bfdffe-c945-4dc6-8e3f-e61105a05537) | 5,524 | 94 | 5.3B | openmdw-1.1 | 2026-09-25 | | [Qwen3.5-0.8B-weight-space-programming](https://mindket.com/p/099470c8-44f0-4966-a399-6bb1eb1cb116) | 4,374 | 137 | 873M | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-03 - First published: 2026-10-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open dBenf/EarthJev on Hugging Face](https://huggingface.co/dBenf/EarthJev) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d973b9a2-651e-4bec-8aa3-966815476618) - [Its page on Mindket](https://mindket.com/p/d973b9a2-651e-4bec-8aa3-966815476618)