Update README.md

README.md

@@ -70,18 +70,151 @@ configs:
   - split: test
     path: data/test-*
 license: cc-by-4.0
+tags:
+- vision
+- ai
+- climate
+- forest
+- tree
+- remote_sensing
+size_categories:
+- 10K<n<100K
 ---
 
-## SelvaBox: A high-resolution dataset for tropical tree
+---
+
+# SelvaBox: A high-resolution dataset for tropical tree
 
 <p align="left">
-  <img src="./teaser_wo_name.png" width="1200"/>
+  <img src="./teaser_wo_name.png" width="800"/>
 </p>
 
-This is the version of the SelvaBox dataset that has been pre-processed and presented in our SelvaBox paper.
+<!-- Provide a quick summary of the dataset. -->
 
+This is the version of the SelvaBox dataset that has been pre-processed and presented in our SelvaBox paper.
 The dataset is made of 14 rasters resampled at 4.5 cm GSD, from three different countries: Brazil, Ecuador and Panama. It comprises over 83 000 human bounding box annotations for tropical tree crowns in dense canopies.
 
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
 Training tiles are 3555x3555 pixels, while validation and test tiles are 1777x1777 pixels, equivalent to 80x80 meters spatial extent. There is 50% overlap between train and validation tiles, and 75% between test tiles (to ensure that the largest trees of 50+ meters in diameter will fit entirely in at least one tile).
 
-Link to our paper will come soon.
+- **Curated by:** Will be added after double-blind review.
+- **Funded by [optional]:** Will be added after double-blind review.
+- **License:** CC BY 4.0
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** Will be added after double-blind review.
+- **Paper:** Will be added after double-blind review.
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+[More Information Needed]
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+[More Information Needed]
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+Here is an overview of the different orthomosaics that were pre-processed and tiled to produce SelvaBox:
+| Raster Name     | Drone    | Country | Date       | Sky Conditions | GSD (cm/px) | Forest Type            | # Hectares | # Annotations | Proposed Split(s)  |
+| --------------- | -------- | ------- | ---------- | -------------- | ----------- | ---------------------- | ---------: | ------------: | ------------------ |
+| zf2quad         | m3m      | Brazil  | 2024-01-30 | clear          | 2.3         | primary                |       15.5 |         1,343 | valid              |
+| zf2tower        | m3m      | Brazil  | 2024-01-30 | clear          | 2.2         | primary                |        9.5 |         1,716 | test               |
+| zf2transectew   | m3m      | Brazil  | 2024-01-30 | clear          | 1.5         | primary                |        2.6 |           359 | train              |
+| zf2campinarana  | m3m      | Brazil  | 2024-01-31 | clear          | 2.3         | primary                |         66 |        16,396 | train              |
+| transectotoni   | mavicpro | Ecuador | 2017-08-10 | cloudy         | 4.3         | primary                |        4.3 |         5,119 | train              |
+| tbslake         | m3m      | Ecuador | 2023-05-25 | clear          | 5.1         | primary                |         19 |         1,279 | train, test        |
+| sanitower       | mini2    | Ecuador | 2023-09-11 | cloudy         | 1.8         | primary                |        5.8 |         1,721 | train              |
+| inundated       | m3e      | Ecuador | 2023-10-18 | cloudy         | 2.2         | primary                |         68 |         9,075 | train, valid, test |
+| pantano         | m3e      | Ecuador | 2023-10-18 | cloudy         | 1.9         | primary                |         41 |         4,193 | train              |
+| terrafirme      | m3e      | Ecuador | 2023-10-18 | clear          | 2.4         | primary                |        110 |         6,479 | train              |
+| asnortheast     | m3m      | Panama  | 2023-12-07 | partial cloud  | 1.3         | plantations, secondary |         33 |        12,930 | train, valid, test |
+| asnorthnorth    | m3m      | Panama  | 2023-12-07 | cloud          | 1.2         | plantations, secondary |         15 |         6,020 | train              |
+| asforestnorthe2 | m3m      | Panama  | 2023-12-08 | clear          | 1.5         | secondary              |         20 |         5,925 | valid, test        |
+| asforestsouth2  | m3m      | Panama  | 2023-12-08 | clear          | 1.6         | secondary              |         28 |        10,582 | train              |
+
+### Annotations
+
+SelvaBox is the largest tropical tree detection dataset, one order-of-magnitude larger than existing ones (mainly BCI50ha and Detectree2). It is also the 2nd largest tree detection dataset overall in annotation count, after OAM-TCD.
+| Name                   | # Trees | GSD (cm)    | Type         | Biome        |
+| ---------------------- | ------: | ----------- | ------------ | ------------ |
+| NeonTreeEval. \[1]     |     16k | 10          | natural      | temperate    |
+| ReforesTree \[2]       |    4.6k | 2           | plantation   | tropical     |
+| Firoze *et al.* \[3]   |    6.5k | 2–5         | natural      | temperate    |
+| Detectree2 \[4]        |    3.8k | 10          | natural      | tropical     |
+| BCI50ha \[5]           |    4.7k | 4.5         | natural      | tropical     |
+| BAMFORESTS \[6]        |     27k | 1.6–1.8     | natural      | temperate    |
+| QuebecTrees \[7]       |     23k | 1.9         | natural      | temperate    |
+| Quebec Plantation \[8] |   19.6k | 0.5         | plantation   | temperate    |
+| OAM-TCD \[9]           |    280k | 10          | mostly urban | worldwide    |
+| **SelvaBox (ours)**    | **83k** | **1.2–5.1** | **natural**  | **tropical** |
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+The annotations have been created by five domain experts with exact same instructions and all started with a demo and an annotation practice beforehand. All annotations have been made in ArcGIS Pro with ArcGIS Online layers to track online work of two annotators working on the same orthomosaic simultaneously. In large and dense areas, one or several annotators have performed an additional pass over the orthomosaic to annotate potential missing trees. Once annotations were completed by one or several annotators, up to two domain experts performed quality control steps for all annotations of each orthomosaic by following precise guidelines:
+
+- A- Setup a 60x60m grid cell over the orthomosaic.
+- B- Proceed to the verification by systematically scanning each cell to avoid missing any areas.
+- C- Ensure that there are as many annotated trees as possible in each cell.
+- D- Also annotate dead/leafless trees.
+- E- Check that annotations already completed are correct, adjusting them if necessary.
+
+All annotators and reviewers were provided with documentation with difficult use cases as a reference when they were uncertain on the annotation procedure. As a comparison, one may note that annotations in OAM-TCD (NeurIPS 2024) [2] were created by professional annotators that were not domain experts, and a part of these annotations were then reviewed by ecology experts.
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+Will be added after double-blind review.
+
+<!-- #### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+<!-- [More Information Needed] -->
+
+<!-- ## Bias, Risks, and Limitations -->
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+<!-- [More Information Needed] --> -->
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+Will be added after double-blind review.
+
+## Dataset Card Contact
+
+Will be added after double-blind review.