TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards
Paper • 2310.02162 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: string>, _media_type (... 467 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: string>, _media_type: string, _ (... 455 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: string>
child 0, item: string
child 3, _media_type: string
child 4, _rand: double
child 5, site: string
child 6, session_id: string
child 7, platform: string
child 8, lidar_model: string
child 9, description: string
child 10, duration_s: double
child 11, message_count: int64
child 12, channel_count: int64
child 13, topics: list<item: string>
child 0, item: string
child 14, schemas: list<item: string>
child 0, item: string
child 15, has_image: bool
child 16, has_pointcloud: bool
child 17, has_gps: bool
child 18, has_imu: bool
child 19, has_logs: bool
child 20, has_unrecognized_schema: bool
child 21, _dataset_id: struct<$oid: string>
child 0, $oid: string
child 22, created_at: struct<$date: string>
child 0, $date: string
child 23, last_modified_at: struct<$date: string>
child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_t
...
e: string, ftype: string, embedded_doc_type: string, subfield: string, fields: (... 318 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
tags: list<item: null>
child 0, item: null
last_loaded_at: struct<$date: string>
child 0, $date: string
media_type: string
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List({'_id': {'$oid': Value('string')}, '_dataset_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'view_stages': List(Value('string')), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_loaded_at': {'$date': Value('string')}}), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'training_runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: string>, _media_type (... 467 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: string>, _media_type: string, _ (... 455 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: string>
child 0, item: string
child 3, _media_type: string
child 4, _rand: double
child 5, site: string
child 6, session_id: string
child 7, platform: string
child 8, lidar_model: string
child 9, description: string
child 10, duration_s: double
child 11, message_count: int64
child 12, channel_count: int64
child 13, topics: list<item: string>
child 0, item: string
child 14, schemas: list<item: string>
child 0, item: string
child 15, has_image: bool
child 16, has_pointcloud: bool
child 17, has_gps: bool
child 18, has_imu: bool
child 19, has_logs: bool
child 20, has_unrecognized_schema: bool
child 21, _dataset_id: struct<$oid: string>
child 0, $oid: string
child 22, created_at: struct<$date: string>
child 0, $date: string
child 23, last_modified_at: struct<$date: string>
child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_t
...
e: string, ftype: string, embedded_doc_type: string, subfield: string, fields: (... 318 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
tags: list<item: null>
child 0, item: null
last_loaded_at: struct<$date: string>
child 0, $date: string
media_type: string
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List({'_id': {'$oid': Value('string')}, '_dataset_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'view_stages': List(Value('string')), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_loaded_at': {'$date': Value('string')}}), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'training_runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for TreeScope (MCAP)
This is a FiftyOne dataset with 10 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/treescope-vat0723-multimodal")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
This dataset is a curated, 10-episode multimodal subset of the TreeScope
v1.0 robotics dataset, repackaged as time-synchronized MCAP
recordings for FiftyOne's native multimodal dataset support
(introduced in FiftyOne 1.19). Each sample is one continuous UAV flight
episode from TreeScope's VAT-0723 site (Appomattox-Buckingham State Forest,
Virginia), viewable in FiftyOne's tiled multimodal viewer with synchronized
camera, LiDAR point cloud, GPS, IMU, diagnostics, and log playback.
TreeScope itself is a LiDAR dataset for precision agriculture and forestry, collected with UAV and mobile robot platforms across six forest and orchard sites, with manually-annotated semantic labels and field-measured tree diameters for benchmarking segmentation and diameter-estimation algorithms. This repackaging uses only the raw sensor streams from one site — it does not carry over TreeScope's original semantic-segmentation or DBH ground truth (see Dataset Creation).
- Curated by: Derek Cheng, Fernando Cladera, Ankit Prabhu, Xu Liu, Alan Zhu, Pratik Chaudhari, and Vijay Kumar (GRASP Laboratory, University of Pennsylvania); P. Corey Green (Virginia Tech, Forest Resources and Environmental Conservation); Reza Ehsani (UC Merced, Mechanical Engineering) — original TreeScope v1.0 data collection. This MCAP/FiftyOne multimodal repackaging (episode merging, tile-coverage curation, dataset card) was prepared independently by Harpreet Sahota.
- Funded by: IoT4Ag ERC, funded by the National Science Foundation (NSF) under NSF Cooperative Agreement Number EEC-1941529; NIFA grant 2022-67021-36856; NSF grant CCR-2112665; and C-BRIC, a Semiconductor Research Corporation Joint University Microelectronics Program cosponsored by DARPA.
- Shared by: Harpreet Sahota (this repackaging); the original TreeScope v1.0 dataset is shared by its authors at https://treescope.org.
- Language(s): N/A (sensor data — LiDAR, imagery, GPS, IMU; no text).
- License: CC BY-NC-SA 4.0, inherited from the source TreeScope v1.0 release (non-commercial use only).
Dataset Sources
- Repository: https://github.com/KumarRobotics/treescope
- Paper: TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards (arXiv:2310.02162)
- Demo: https://treescope.org (original full dataset release)
Uses
Direct Use
- Exercising and demoing FiftyOne's multimodal MCAP support: synchronized playback of camera, 3D point cloud, map/GPS, plot, logs, and raw-message tiles across real robotics recordings.
- Browsing/inspecting under-canopy UAV forestry LiDAR flights (manual and autonomous) — point clouds, flight trajectories, and (for 6 of the 10 episodes) stereo camera and IMU streams.
- Prototyping analyses against the raw sensor streams themselves, e.g.
reviewing GPS flight tracks, comparing onboard vs. offline-refined
odometry (
/Odometryvs./refined/Odometry), or plotting IMU/diagnostic time series — using FiftyOne's per-episode metadata fields to filter which episodes have the streams of interest.
Out-of-Scope Use
- Reproducing TreeScope's official semantic-segmentation or diameter-at- breast-height (DBH) benchmark results — no ground-truth labels are attached to any of these 10 episodes (see Dataset Creation).
- Treating this as a representative sample of TreeScope v1.0 as a whole —
it is 10 of the many
VAT-0723Uepisodes at one of TreeScope's six sites, selected for disk-space and multimodal tile-coverage reasons, not statistical sampling. - Commercial use, which is precluded by the source CC BY-NC-SA 4.0 license.
Dataset Structure
This is a flat (ungrouped) FiftyOne dataset with media_type: "multimodal"
and 10 samples. Each sample is one episode — a single continuous UAV
flight recording — stored as one merged .mcap file; FiftyOne infers the
multimodal media type automatically from the .mcap file extension. There
are no video frames or point-cloud files as separate FiftyOne samples: the
episode is the sample unit, and every stream inside it (camera, LiDAR,
GPS, IMU, logs, etc.) is decoded live by FiftyOne's multimodal viewer.
Per-sample tags mark flight mode: manual_flight (4 samples) or
autonomous_flight (6 samples). The dataset itself carries no dataset-level
tags, and dataset.info is empty (no extra dataset-level metadata beyond the
per-sample fields below).
Fields
| Field | FiftyOne type | Description |
|---|---|---|
filepath |
StringField |
Absolute path to the episode's merged .mcap file — the sample's multimodal media |
tags |
ListField(StringField) |
Flight-mode tag: manual_flight or autonomous_flight |
site |
StringField |
TreeScope site name; always "VAT-0723" in this subset |
session_id |
StringField |
TreeScope's original session identifier (e.g. VAT-0723U-AUTO-01), verbatim from metadata/VAT-0723.json |
platform |
StringField |
Flight mode + platform, "ULS Manual" or "ULS Autonomous" (ULS = UAV Laser Scanning), verbatim from source metadata's attributes.type |
lidar_model |
StringField |
LiDAR sensor model; always "OS1-64" (Ouster OS1-64) for this UAV subset |
description |
StringField |
One-line, human-written summary of the episode (source bag identity + notable sensor coverage) |
duration_s |
FloatField |
Episode duration in seconds, computed from the merged MCAP's message-time span |
message_count |
IntField |
Total MCAP message count across all channels in the episode |
channel_count |
IntField |
Total MCAP channel (topic) count in the episode |
topics |
ListField(StringField) |
Every ROS topic name present in the episode's MCAP |
schemas |
ListField(StringField) |
Every distinct ROS message schema name present in the episode's MCAP |
has_image |
BooleanField |
Whether the episode has a camera stream FiftyOne's Image tile can decode (schema-based: sensor_msgs/msg/(Compressed)Image) |
has_pointcloud |
BooleanField |
Whether it has a decodable point-cloud stream for the 3D tile (sensor_msgs/msg/PointCloud2) |
has_gps |
BooleanField |
Whether it has a decodable GPS fix stream for the Map tile (sensor_msgs/msg/NavSatFix) |
has_imu |
BooleanField |
Whether it has a decodable IMU stream for the Plot tile (sensor_msgs/msg/Imu) |
has_logs |
BooleanField |
Whether it has a decodable log stream for the Logs tile (rosgraph_msgs/msg/Log) |
has_unrecognized_schema |
BooleanField |
Whether the episode also contains a schema with no built-in FiftyOne decoder (Message-tile-only, e.g. raw Ouster LiDAR packets, custom quadrotor autonomy messages) |
Standard FiftyOne bookkeeping fields (id, metadata, created_at,
last_modified_at) are also present but not source-specific.
Label types and why
No FiftyOne label fields are attached to this dataset. The actual
multimodal content — camera images, LiDAR point clouds, GPS tracks, IMU
readings, and logs — lives entirely inside each sample's .mcap file and is
decoded live by FiftyOne's multimodal viewer (Image, 3D, Map, Plot, Logs, and
Message tiles), not represented as separate Detection/Segmentation/etc.
label objects. The has_* boolean fields above exist purely so users can
filter or query episodes by which tiles they'll populate, without opening
every MCAP file first — e.g. dataset.match(F("has_image") & F("has_imu")).
A saved view, full_multimodal_showcase, selects the 9 of 10 episodes
that have every tile type populated with real, decodable data (Image, 3D,
Map, Plot-capable IMU, Logs, Message); the excluded episode
(VAT-0723U-01) genuinely lacks camera and decoded-IMU topics, kept in the
full dataset for realistic sensor-coverage heterogeneity.
Schemas present across episodes
Every episode has, at minimum: nav_msgs/msg/Odometry,
sensor_msgs/msg/PointCloud2 (multiple point-cloud topics — raw
segmented, tree-only, ground-only, and world-frame variants),
sensor_msgs/msg/NavSatFix, tf2_msgs/msg/TFMessage,
rosgraph_msgs/msg/Log, diagnostic_msgs/msg/DiagnosticArray, and
undecoded ouster_ros/msg/PacketMsg (raw LiDAR/IMU packets, Message-tile
only). The 9 richer episodes additionally have
sensor_msgs/msg/CompressedImage (stereo cameras), sensor_msgs/msg/Imu
and MagneticField (decoded IMU/mag), and custom autonomy schemas
(kr_mav_msgs/msg/PositionCommand, planning_ros_msgs/msg/{VoxelMap,Path,Trajectory})
from the autonomous flights' quadrotor planner — all Message-tile fodder,
since these are non-standard schemas with no built-in FiftyOne decoder.
Parsing decisions
- One sample = one episode. Each sample corresponds to a single, continuous flight recording, never split into per-frame or per-message samples — FiftyOne's multimodal viewer handles playback within an episode.
- Each episode's MCAP is a merge of that session's raw + processed ROS1
bag pair, not a straight conversion of one bag, because:
- Both bags independently publish
/Odometryand/tf— the raw bag's onboard/live estimate vs. the processed bag's offline Faster-LIO-refined trajectory. The processed bag's copies are remapped to/refined/Odometryand/refined/tfso both trajectories stay distinguishable rather than colliding into one interleaved channel. - Both bags also independently publish
/ublox/fixand/ublox/fix_velocityas byte-identical pass-through copies. Unlike the odometry case there is no "refined" GPS variant to preserve, so the processed bag's copies are dropped entirely — each GPS fix appears exactly once in the merged MCAP. std_msgs/msg/Headerin the shared destination typestore is explicitly overridden with the canonical ROS 2 definition, because ROS1'sHeadercarries an extrauint32 seqfield that silently corrupts CDR offset/size calculations for any nested-Header message type if copied verbatim from the ROS1 side.- The processed bag's message timestamps are rebased by a constant offset
(
raw_bag.start_time - processed_bag.start_time), because they carry the wall-clock time of the offline reprocessing job (recorded weeks to months after the actual flight) rather than the flight's capture time. Left uncorrected, an episode would nominally span that entire gap with real data in only two tiny slivers — unusable for scrubbing/playback.
- Both bags independently publish
- Candidate episodes were verified before download, not after: a
session's raw bag is inspected via HTTP
Rangerequests against its ROS1 bag-format connection records (topics + schemas, ~100 KB fetched instead of gigabytes) before committing to the full download. This also detects corrupted/unindexed bags — one candidate,VAT-0723U-06, was excluded this way: its bag header's declared index position lies beyond the file's actual size, indicating a split/concatenated recording unparseable without a full download. - No ground truth is attached to any of these 10 episodes, deliberately.
The site's semantic-segmentation labels
(
ground_truth/labels/VAT-0723U.h5) were downloaded and checked, but their labeled-frame timestamps fall entirely outside every chosen episode's actual flight window;metadata/VAT-0723.jsonindependently confirmsattributes.semantic_labels=falseandattributes.dbh=falsefor all 10 sessions. FiftyOne's temporal tags were skipped for the same reason — nothing here is faked to look annotated. - This is a small subset of one TreeScope site. 10 of the (more
numerous, and individually much larger)
VAT-0723Uepisodes, at one of TreeScope's six sites. Selection was constrained by local disk space and by wanting genuine multimodal tile-coverage diversity, not by scientific sampling. FiftyOne Enterprise-only multimodal features (MCAP indexing, projections, derived event/label timeline tracks) are out of scope entirely — this dataset only exercises open-source FiftyOne capability.
Dataset Creation
Curation Rationale
VAT-0723 was chosen as the only confirmed TreeScope site with GPS
(NavSatFix) data released, making FiftyOne's Map tile achievable
faithfully (GPS hardware is mentioned in the TreeScope paper but was found
missing from the released bags at other inspected sites). Its raw+processed
bag pairs are also small enough, relative to TreeScope's other sites
(hundreds of GB to ~1 TB each), to download a meaningful multi-episode
subset without approaching the full ~760 GB site or ~2.2 TB dataset. The
selection and MCAP-merge pipeline were built to exercise every
non-Enterprise FiftyOne multimodal viewer tile (Image, 3D, Map, Plot, Logs,
Message) with real, decodable data — not to support TreeScope's original
segmentation or DBH benchmarks.
Source Data
Data Collection and Processing
Per the TreeScope paper: VAT-0723's UAV (ULS) data was collected with the
Falcon 4 UAV (4.2 kg, up to 30 minutes of under-canopy flight), equipped
with an Ouster OS1-64 LiDAR (Rev 6; 64 vertical channels, 1024
horizontal points, 120 m range, 45° vertical FoV), an Open Vision Computer
(stereo cameras), a VectorNav VN-100 IMU, and a UBlox ZED-F9P GPS, flown
over intensively-managed loblolly pine plots in the
Appomattox-Buckingham State Forest, Virginia. Raw ROS1 bags contain the
onboard sensor streams as captured; processed ROS1 bags contain
Faster-LIO-derived lidar-inertial
odometry and velocity-corrected point-cloud sweeps, plus a
RangeNet++-based semantic
ground/tree-stem split.
For this repackaging, each episode's raw and processed ROS1 bag were merged
into one .mcap file using the rosbags
Python library (see Parsing decisions for the exact
transforms applied), then loaded into FiftyOne with the per-sample metadata
fields described above. No sensor data was synthesized or altered beyond
the topic remapping/dropping and timestamp rebasing documented above.
Who are the source data producers?
The GRASP Laboratory, University of Pennsylvania (Falcon 4 UAV platform and data collection), for the original TreeScope v1.0 release.
Annotations
Annotation process
None. No semantic-segmentation labels, DBH ground truth, or temporal tags are attached to any of these 10 episodes — see Parsing decisions for why.
Who are the annotators?
N/A — this subset carries no annotations.
Personal and Sensitive Information
None identified. The GPS stream in each episode records the UAV's flight path over a forest research site, not any human or personally identifiable location.
Citation
BibTeX:
@misc{cheng2023treescope,
title={TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards},
author={Derek Cheng and Fernando Cladera and Ankit Prabhu and Xu Liu and Alan Zhu and P. Corey Green and Reza Ehsani and Pratik Chaudhari and Vijay Kumar},
year={2023},
eprint={2310.02162},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
APA:
Cheng, D., Cladera, F., Prabhu, A., Liu, X., Zhu, A., Green, P. C., Ehsani, R., Chaudhari, P., & Kumar, V. (2023). TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards. arXiv preprint arXiv:2310.02162.
More Information
This repository is an independently-curated, derived subset of the official TreeScope v1.0 release, repackaged as MCAP for FiftyOne's multimodal support. It is not an official TreeScope artifact. For the full dataset (all six sites, raw and processed bags, semantic labels, and DBH ground truth), see https://treescope.org and https://github.com/KumarRobotics/treescope.
Dataset Card Authors
Harpreet Sahota (@harpreetsahota) — MCAP repackaging and this card. Original dataset authors are listed under Dataset Description.
Dataset Card Contact
Harpreet Sahota — https://huggingface.co/harpreetsahota
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