Datasets:

z-hb
/

OpenSatMap

@@ -9,11 +9,20 @@ task_categories:
 - image-segmentation
 ---
 # OpenSatMap Dataset Card
 ## Description
 The dataset contains 3,787 high-resolution satellite images with fine-grained annotations, covering diverse geographic locations and popular driving datasets. It can be used for large-scale map construction and downstream tasks like autonomous driving. The images are collected from Google Maps at level 19 resolution (0.3m/pixel) and level 20 resolution (0.15m/pixel), we denote them as OpenSatMap19 and OpenSatMap20, respectively. For OpenSatMap19, the images are collected from 8 cities in China, including Beijing, Shanghai, Guangzhou, ShenZhen, Chengdu, Xi'an, Tianjin, and Shenyang. There are 1806 images in OpenSatMap19. For OpenSatMap20, the images are collected from 18 countries, more than 50 cities all over the world. There are 1981 images in OpenSatMap20. The figure below shows the sampling areas of the images in OpenSatMap.
 For each image, we provide instance-level annotations and eight attributes for road structures, including lanes lines, curb and virtual lines. The instances in OpenSatMap images are annotated by experts in remote sensing and computer vision. We will continue to update the dataset, to grow in size and scope to reflect evolving real-world conditions.
 ## Image Source and Usage License
 The OpenSatMap images are collected from Google Maps. The dataset will be licensed under a Creative Commons CC-BY-NC-SA 4.0 license and the usage of the images must respect the Google Maps Terms of Service.
 ## Line Category and Attribute
@@ -32,6 +41,10 @@ Specifically, they are:
 - Clearness: The clearness of the line. It can be clear or fuzzy.
 Note that there is no man-made visible line on curbs and virtual lines, so we annotate their colors, line types, numbers of lines, and functions as none.
 ## Annotation Format
 The annotations are stored in JSON format. Each image is annotated with "image_width", "image_height", and a list of "lines" where the elements are line instances. Each line is annotated with "category", "points", "color", "line_type", "line_num", "function", "bidirection", "boundary", "shaded", and "clearness".
 ```
@@ -108,5 +121,18 @@ The meta data of GPS coordinates and image acquisition time are also provided. T
 ## Intended use
 ### Task 1: Instance-level Line Detection
 The aim of this task is to extract road structures from satellite images at the instance level. For each instance, we use polylines as the vectorized representation and pixel-level masks as the rasterized representation.
 ### Task 2: Satellite-enhanced Online Map Construction
-We use satellite images to enhance online map construction for autonomous driving. Inputs are carema images of an autonomous vehicle and satellite images of the same area and outputs are vectorized map elements around the vehicle.

 - image-segmentation
 ---
 # OpenSatMap Dataset Card
+<p align="center">
+  <img src="image/README/1732438503023.png" alt="1732438503023">
+</p>
 ## Description
 The dataset contains 3,787 high-resolution satellite images with fine-grained annotations, covering diverse geographic locations and popular driving datasets. It can be used for large-scale map construction and downstream tasks like autonomous driving. The images are collected from Google Maps at level 19 resolution (0.3m/pixel) and level 20 resolution (0.15m/pixel), we denote them as OpenSatMap19 and OpenSatMap20, respectively. For OpenSatMap19, the images are collected from 8 cities in China, including Beijing, Shanghai, Guangzhou, ShenZhen, Chengdu, Xi'an, Tianjin, and Shenyang. There are 1806 images in OpenSatMap19. For OpenSatMap20, the images are collected from 18 countries, more than 50 cities all over the world. There are 1981 images in OpenSatMap20. The figure below shows the sampling areas of the images in OpenSatMap.
+<p align="center">
+  <img src="image/README/1732438352223.png" alt="1732438352223">
+</p>
 For each image, we provide instance-level annotations and eight attributes for road structures, including lanes lines, curb and virtual lines. The instances in OpenSatMap images are annotated by experts in remote sensing and computer vision. We will continue to update the dataset, to grow in size and scope to reflect evolving real-world conditions.
 ## Image Source and Usage License
 The OpenSatMap images are collected from Google Maps. The dataset will be licensed under a Creative Commons CC-BY-NC-SA 4.0 license and the usage of the images must respect the Google Maps Terms of Service.
 ## Line Category and Attribute
 - Clearness: The clearness of the line. It can be clear or fuzzy.
 Note that there is no man-made visible line on curbs and virtual lines, so we annotate their colors, line types, numbers of lines, and functions as none.
+<p align="center">
+  <img src="image/README/1732438442673.png" alt="1732438442673">
+</p>
 ## Annotation Format
 The annotations are stored in JSON format. Each image is annotated with "image_width", "image_height", and a list of "lines" where the elements are line instances. Each line is annotated with "category", "points", "color", "line_type", "line_num", "function", "bidirection", "boundary", "shaded", and "clearness".
 ```
 ## Intended use
 ### Task 1: Instance-level Line Detection
 The aim of this task is to extract road structures from satellite images at the instance level. For each instance, we use polylines as the vectorized representation and pixel-level masks as the rasterized representation.
+<p align="center">
+  <img src="image/README/1732438334686.png" alt="1732438334686">
+</p>
 ### Task 2: Satellite-enhanced Online Map Construction
+We use satellite images to enhance online map construction for autonomous driving. Inputs are carema images of an autonomous vehicle and satellite images of the same area and outputs are vectorized map elements around the vehicle.
+<p align="center">
+  <img src="image/README/1732438311510.png" alt="1732438311510">
+</p>
+**Alignment with driving benchmark (nuScenes)**
+<p align="center">
+  <img src="image/README/1732438587349.png" alt="1732438587349">
+</p>