Datasets:

juanpac96
/

urban_tree_census_data

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Tabular Classification

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train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
epsg: int64
unit: string
geodetic_crs: string
datum: string
ellipsoid: string
prime_meridian: string
data_source: string
information_source: string
revision_date: string
scope: string
area_of_use: string
coordinate_system: string
vs
record_code: int64
measurement_date_event: string
measurement_name: string
measurement_value: double
measurement_method: string
measurement_unit: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 520, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              epsg: int64
              unit: string
              geodetic_crs: string
              datum: string
              ellipsoid: string
              prime_meridian: string
              data_source: string
              information_source: string
              revision_date: string
              scope: string
              area_of_use: string
              coordinate_system: string
              vs
              record_code: int64
              measurement_date_event: string
              measurement_name: string
              measurement_value: double
              measurement_method: string
              measurement_unit: string

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Urban Tree Census Data

This dataset was collected as part of the Urban Tree Observatory Project in Ibagué, Colombia.
It includes georeferenced records of urban trees, taxonomic details, physical measurements, and observational data. the objective is create a PostgreSQL database using Django.

Biodiversity_records.csv: Main biodiversity registry.
Place.csv: Geographical locations where observations were made.
Geog_coord_syst.csv: Metadata about the coordinate reference systems used.
Taxonomy_details.csv: Taxonomic classification per tree (family, genus, species).
Measurements.csv: Tree measurements such as trunk height, total height, crown diameter, DBH, and volume.
Observations_details.csv: Observations including IUCN status, physical condition, origin, use, and other descriptive attributes.
FunctionalTraitsStructure.csv: Functional traits assigned to each taxonomic unit, including canopy shape, leaf color, maximum height, crown diameter, and indices such as carbon sequestration and shade potential.

The objective is to build and populate a structured PostgreSQL database using Python and SQLAlchemy, which stores biodiversity, taxonomy, and urban tree census data for Ibagué, Colombia.

Datasets:

juanpac96
/

urban_tree_census_data

Urban Tree Census Data

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