Add trial segment

Images/Town01.txt

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README.md

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 ---
 license: mit
+language:
+- en
 ---
 
 <div align="center">
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 <!-- [![Watch the Demo](./assets/robust_aerial_videos.mp4)](./assets/robust_aerial_videos.mp4) -->
 
 <img src="./assets/skyscene_intro_teaser.png" width="100%"/>
+
+## Dataset Summary
+
+Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. 
+Due to inherent challenges in obtaining such images in controlled real-world settings,
+we present SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV) perspectives. 
+**SkyScenes** images are carefully curated from **CARLA** to comprehensively capture diversity across layout (urban and rural maps), weather conditions, times of day, pitch angles and altitudes with corresponding semantic, instance and depth annotations. 
+**SkyScenes** features **33,600** images in total, which are spread across 8 towns, 5 weather and daytime conditions and 12 height and pitch variations. 
+
+
+<details>
+  <summary>Click to view the detailed list of all variations</summary>
+  
+  - **Layout Variations(Total 8):**:
+    - Town01
+    - Town02
+    - Town03
+    - Town04
+    - Town05
+    - Town06
+    - Town07 
+    - Town10HD
+  _Town07 features Rural Scenes, whereas the rest of the towns feature Urban scenes_
+
+- **Weather & Daytime Variations(Total 5):**
+  - ClearNoon
+  - ClearSunset
+  - ClearNight
+  - CloudyNoon
+  - MidRainyNoon
+
+- **Height and Pitch Variations of UAV Flight(Total 12):**
+  - Height = 15m, Pitch = 0°
+  - Height = 15m, Pitch = 45°
+  - Height = 15m, Pitch = 60°
+  - Height = 15m, Pitch = 90°
+  - Height = 35m, Pitch = 0°
+  - Height = 35m, Pitch = 45°
+  - Height = 35m, Pitch = 60°
+  - Height = 35m, Pitch = 90°
+  - Height = 60m, Pitch = 0°
+  - Height = 60m, Pitch = 45°
+  - Height = 60m, Pitch = 60°
+  - Height = 60m, Pitch = 90°
+</details>
+<details>
+<summary>Click to view class definitions, color palette and class IDs for Semantic Segmentation</summary>
+
+**SkyScenes** semantic segmentation labels span 28 classes which can be further collapsed to 20 classes. 
+| Class ID | Class ID (collapsed) | RGB Color Palette | Class Name       | Definition                                                                                         |
+|----------|--------------------|-------------------|------------------|----------------------------------------------------------------------------------------------------|
+| 0        | -1                 | <span style="color:rgb(0, 0, 0)"> (0, 0, 0) </span>         | unlabeled        | Elements/objects in the scene that have not been categorized                                           |
+| 1        | 2                  | <span style="color:rgb(70, 70, 70)"> (70, 70, 70) </span>      | building         | Includes houses, skyscrapers, and the elements attached to them                                        |
+| 2        | 4                  | <span style="color:rgb(190, 153, 153)"> (190, 153, 153) </span>   | fence            | Wood or wire assemblies that enclose an area of ground                                                 |
+| 3        | -1                  | <span style="color:rgb(55, 90, 80)"> (55, 90, 80) </span>      | other            | Uncategorized elements                                                                              |
+| 4        | 11                  | <span style="color:rgb(220, 20, 60)"> (220, 20, 60) </span>     | pedestrian       | Humans that walk                                                                                    |
+| 5        | 5                  | <span style="color:rgb(153, 153, 153)"> (153, 153, 153) </span>  | pole             | Vertically oriented pole and its horizontal components if any                                           |
+| 6        | 16                  | <span style="color:rgb(157, 234, 50)"> (157, 234, 50) </span>    | roadline         | Markings on road                                                                                    |
+| 7        | 0                  | <span style="color:rgb(128, 64, 128)"> (128, 64, 128) </span>    | road             | Lanes, streets, paved areas on which cars drive                                                       |
+| 8        | 1                  | <span style="color:rgb(244, 35, 232)"> (244, 35, 232) </span>    | sidewalk         | Parts of ground designated for pedestrians or cyclists                                                 |
+| 9        | 8                  | <span style="color:rgb(107, 142, 35)"> (107, 142, 35) </span>    | vegetation       | Trees, hedges, all kinds of vertical vegetation (ground-level vegetation is not included here)       |
+| 10       | 13                 | <span style="color:rgb(0, 0, 142)"> (0, 0, 142) </span>       | cars             | Cars in scene                                                                                       |
+| 11       | 3                 | <span style="color:rgb(102, 102, 156)"> (102, 102, 156) </span>   | wall             | Individual standing walls, not part of buildings                                                      |
+| 12       | 7                 | <span style="color:rgb(220, 220, 0)"> (220, 220, 0) </span>     | traffic sign     | Signs installed by the state/city authority, usually for traffic regulation                            |
+| 13       | 10                 | <span style="color:rgb(70, 130, 180)"> (70, 130, 180) </span>    | sky              | Open sky, including clouds and sun                                                                   |
+| 14       | -1                 | <span style="color:rgb(81, 0, 81)"> (81, 0, 81) </span>       | ground           | Any horizontal ground-level structures that do not match any other category                            |
+| 15       | -1                 | <span style="color:rgb(150, 100, 100)"> (150, 100, 100) </span>  | bridge           | The structure of the bridge                                                                          |
+| 16       | -1                 | <span style="color:rgb(230, 150, 140)"> (230, 150, 140) </span>  | railtrack        | Rail tracks that are non-drivable by cars                                                            |
+| 17       | -1                 | <span style="color:rgb(180, 165, 180)"> (180, 165, 180) </span>  | guardrail        | Guard rails / crash barriers                                                                         |
+| 18       | 6                 | <span style="color:rgb(250, 170, 30)"> (250, 170, 30) </span>    | traffic light    | Traffic light boxes without their poles                                                              |
+| 19       | -1                 | <span style="color:rgb(110, 190, 160)"> (110, 190, 160) </span>  | static           | Elements in the scene and props that are immovable                                                    |
+| 20       | -1                 | <span style="color:rgb(170, 120, 50)"> (170, 120, 50) </span>    | dynamic          | Elements whose position is susceptible to change over time                                             |
+| 21       | 19                 | <span style="color:rgb(45, 60, 150)"> (45, 60, 150) </span>     | water            | Horizontal water surfaces                                                                            |
+| 22       | 9                 | <span style="color:rgb(152, 251, 152)"> (152, 251, 152) </span>  | terrain          | Grass, ground-level vegetation, soil, or sand                                                         |
+| 23       | 12                 | <span style="color:rgb(255, 0, 0)"> (255, 0, 0) </span>       | rider            | Humans that ride/drive any kind of vehicle or mobility system                                         |
+| 24       | 18                 | <span style="color:rgb(119, 11, 32)"> (119, 11, 32) </span>     | bicycle          | Bicycles in scenes                                                                                  |
+| 25       | 17                 | <span style="color:rgb(0, 0, 230)"> (0, 0, 230) </span>       | motorcycle       | Motorcycles in scene                                                                                |
+| 26       | 15                 | <span style="color:rgb(0, 60, 100)"> (0, 60, 100) </span>      | bus              | Buses in scenes                                                                                     |
+| 27       | 14                 | <span style="color:rgb(0, 0, 70)"> (0, 0, 70) </span>        | truck            | Trucks in scenes                                                                                    |
+                                                                                |
+</details>
+
+## Dataset Structure
+
+The dataset is organized in the following structure:
+<!--<details>
+  <summary><strong>Images (RGB Images)</strong></summary>
+
+  - ***H_15_P_0***
+    - *ClearNoon*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+    - *ClearSunset*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+    - *ClearNight*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+    - *CloudyNoon*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+    - *MidRainyNoon*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+  - ***H_15_P_45***
+    - ...
+  - ...
+  - ***H_60_P_90***
+    - ...
+</details>
+
+<details>
+  <summary><strong>Instance (Instance Segmentation Annotations)</strong></summary>
+
+  - ***H_35_P_45***
+    - *ClearNoon*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+</details>
+
+<details>
+  <summary><strong>Segment (Semantic Segmentation Annotations)</strong></summary>
+
+  - ***H_15_P_0***
+    - *ClearNoon*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+  - ***H_15_P_45***
+    - ...
+  - ...
+  - ***H_60_P_90***
+</details>
+
+<details>
+  <summary><strong>Depth (Depth Annotations)</strong></summary>
+
+  - ***H_35_P_45***
+    - *ClearNoon*
+      - Town01.tar.gz
+      - Town02.tar.gz
+      - ...
+      - Town10HD.tar.gz
+</details>
+-->
+
+
+```
+├── Images (RGB Images)
+    ├── H_15_P_0
+    │   ├── ClearNoon
+    │   │   ├── Town01.tar.gz
+    │   │   ├── Town02.tar.gz
+    │   │   ├── ...
+    │   │   └── Town10HD.tar.gz
+    │   ├── ClearSunset
+    │   │   ├── Town01.tar.gz
+    │   │   ├── Town02.tar.gz
+    │   │   ├── ...
+    │   │   └── Town10HD.tar.gz
+    │   ├── ClearNight
+    │   │   ├── Town01.tar.gz
+    │   │   ├── Town02.tar.gz
+    │   │   ├── ...
+    │   │   └── Town10HD.tar.gz
+    │   ├── CloudyNoon
+    │   │   ├── Town01.tar.gz
+    │   │   ├── Town02.tar.gz
+    │   │   ├── ...
+    │   │   └── Town10HD.tar.gz
+    │   └── MidRainyNoon
+    │       ├── Town01.tar.gz
+    │       ├── Town02.tar.gz
+    │       ├── ...
+    │       └── Town10HD.tar.gz
+    ├── H_15_P_45
+    │   └── ...
+    ├── ...
+    └── H_60_P_90
+        └── ...
+└── Instance (Instance Segmentation Annotations)
+    ├── H_35_P_45
+    │   └── ClearNoon
+    │       ├── Town01.tar.gz
+    │       ├── Town02.tar.gz
+    │       ├── ...
+    │       └── Town10HD.tar.gz
+    └── ...
+└── Segment (Semantic Segmentation Annotations)
+    ├── H_15_P_0
+    │   ├── ClearNoon
+    │   │   ├── Town01.tar.gz
+    │   │   ├── Town02.tar.gz
+    │   │   ├── ...
+    │   │   └── Town10HD.tar.gz
+    │   ├── H_15_P_45
+    │   │   └── ...
+    │   ├── ...
+    │   └── H_60_P_90
+    │       └── ...
+    └── ...
+└── Depth (Depth Annotations)
+    ├── H_35_P_45
+    │   └── ClearNoon 
+    │       ├── Town01.tar.gz
+    │       ├── Town02.tar.gz
+    │       ├── ...
+    │       └── Town10HD.tar.gz
+    └── ...
+```
+    
+**Note**: Since the same viewpoint is reproduced across each weather variation, hence ClearNoon annotations can be used for all images pertaining to the different weather variations.
+
+

SkyScenes.py

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+import collections
+import json
+import os
+
+import datasets
+
+_DESCRIPTION='SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV) perspectives. SkyScenes is curated from CARLA to comprehensively capture diversity across layout (urban and rural maps), weather conditions, times of day, pitch angles and altitudes with corresponding semantic, instance and depth annotations.'
+_HOMEPAGE = "skyscenes.github.io"
+_LICENSE = "MIT"
+# _CITATION = """\
+# @misc{ buildings-instance-segmentation_dataset,
+#     title = { Buildings Instance Segmentation Dataset },
+#     type = { Open Source Dataset },
+#     author = { Roboflow Universe Projects },
+#     howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
+#     url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation },
+#     journal = { Roboflow Universe },
+#     publisher = { Roboflow },
+#     year = { 2023 },
+#     month = { jan },
+#     note = { visited on 2023-01-18 },
+# }
+# """
+_CATEGORIES =    ["unlabeled", "building", "fence", "other", "pedestrian", "pole", 
+                "roadline", "road", "sidewalk", "vegetation", "vehicles", "wall", 
+                "trafficsign", "sky", "ground", "bridge", "railtrack", "guardrail", 
+                "trafficlight", "static", "dynamic", "water", "terrain"]
+
+
+class SKYSCENESConfig(datasets.BuilderConfig):
+    """Builder Config for SkyScenes"""
+
+    def __init__(self, data_urls, metadata_url, **kwargs):
+        """
+        BuilderConfig for SkyScenes.
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SKYSCENESConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+        self.metadata_url = metadata_url
+
+
+class SKYSCENES(datasets.GeneratorBasedBuilder):
+    """satellite-building-segmentation instance segmentation dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        SKYSCENESConfig(
+            name="full",
+            description="Full version of skyscenes dataset.",
+            data_urls="https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_15_P_0/ClearNight/Town01.tar.gz",
+            metadata_url = "https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/Town01.txt",)
+        # SKYSCENESConfig(
+        #     name="mini",
+        #     description="Mini version of satellite-building-segmentation dataset.",
+        #     data_urls=["https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_15_P_0/ClearNight/Town01.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/blob/main/Images/H_15_P_0/ClearNight/Town02.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/blob/main/Images/H_15_P_0/ClearNight/Town03.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/blob/main/Images/H_15_P_0/ClearNight/Town04.tar.gz",]
+        # )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+                {
+                    "image": datasets.Image(),
+                }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(self.config.data_urls)
+        split_metadata_paths = dl_manager.download(self.config.metadata_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(data_files),
+                    "metadata_path": split_metadata_paths,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        """Generate images and labels for splits."""
+        # with open(metadata_path, encoding="utf-8") as f:
+        #     files_to_keep = set(f.read().split("\n"))
+            # print('KEEP',files_to_keep)
+        for file_path, file_obj in images:
+                    # print('FILE',file_path)
+            # if file_path.startswith(_IMAGES_DIR):
+            #     if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep:
+            #         label = file_path.split("/")[2]
+                    yield file_path, {
+                        "image": {"path": file_path, "bytes": file_obj.read()},
+                    }