Datasets:
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Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'time', 'river_discharge'}) and 5 missing columns ({'timestamp', 'status', 'cpu_p', 'phase', 'ram_p'}).
This happened while the csv dataset builder was generating data using
hf://datasets/abdullahashraf122/lucknow_hufp_datasets/lucknow_flood_hist_19_21.csv (at revision cdeae4846bf3a2be7f64cad148e6b53fa76b364d), ['hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/MISSION_CONTROL_LOG.csv', 'hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/lucknow_flood_hist_19_21.csv', 'hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/monte_carlo_paradox_registry.csv', 'hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/monte_carlo_stress_log.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
time: string
river_discharge: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 519
to
{'timestamp': Value('string'), 'phase': Value('string'), 'status': Value('string'), 'cpu_p': Value('float64'), 'ram_p': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'time', 'river_discharge'}) and 5 missing columns ({'timestamp', 'status', 'cpu_p', 'phase', 'ram_p'}).
This happened while the csv dataset builder was generating data using
hf://datasets/abdullahashraf122/lucknow_hufp_datasets/lucknow_flood_hist_19_21.csv (at revision cdeae4846bf3a2be7f64cad148e6b53fa76b364d), ['hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/MISSION_CONTROL_LOG.csv', 'hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/lucknow_flood_hist_19_21.csv', 'hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/monte_carlo_paradox_registry.csv', 'hf://datasets/abdullahashraf122/lucknow_hufp_datasets@cdeae4846bf3a2be7f64cad148e6b53fa76b364d/monte_carlo_stress_log.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
timestamp string | phase string | status string | cpu_p float64 | ram_p float64 |
|---|---|---|---|---|
Sat Apr 4 16:23:53 2026 | MONTE_CARLO | INITIATED | 30.1 | 52.5 |
Sat Apr 4 16:24:00 2026 | MONTE_CARLO | Processed 1,000,000 sims | 83.1 | 53.8 |
Sat Apr 4 16:24:06 2026 | MONTE_CARLO | Processed 2,000,000 sims | 87.6 | 53.6 |
Sat Apr 4 16:24:14 2026 | MONTE_CARLO | Processed 3,000,000 sims | 83.9 | 53.5 |
Sat Apr 4 16:24:21 2026 | MONTE_CARLO | Processed 4,000,000 sims | 85 | 53.5 |
Sat Apr 4 16:24:29 2026 | MONTE_CARLO | Processed 5,000,000 sims | 85.6 | 53.5 |
Sat Apr 4 16:24:37 2026 | MONTE_CARLO | Processed 6,000,000 sims | 86.8 | 54.1 |
Sat Apr 4 16:24:44 2026 | MONTE_CARLO | Processed 7,000,000 sims | 90.2 | 54.2 |
Sat Apr 4 16:24:52 2026 | MONTE_CARLO | Processed 8,000,000 sims | 88.3 | 54.6 |
Sat Apr 4 16:25:00 2026 | MONTE_CARLO | Processed 9,000,000 sims | 86.4 | 54.6 |
Sat Apr 4 16:25:07 2026 | MONTE_CARLO | Processed 10,000,000 sims | 88 | 54 |
Sat Apr 4 16:25:07 2026 | SPATIAL_AUDIT | INITIATED | 42.9 | 53.4 |
Sat Apr 4 16:25:10 2026 | SPATIAL_AUDIT | Confidence: 100.00% | 31.1 | 53.2 |
Sat Apr 4 16:25:10 2026 | MISSION_SUCCESS | REPORT_LOCKED | 66.7 | 53 |
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YAML Metadata Warning:The task_ids "flood-depth-estimation" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
YAML Metadata Warning:The task_ids "radar-backscatter-mapping" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Sentinel-1 SAR 80-Meter Hydrological Intelligence & Telemetry Matrix
Parent GitHub Repository: https://github.com/abdullah00ashraf/sentinel-hufp-v7
Ecosystem Documentation: Axiyon / Producttolaunch Catalog
Dataset Summary
The Sentinel-1 SAR 80-Meter Hydrological Feature Matrix is a multi-sensor earth observation dataset comprising 83,986,875 high-resolution telemetry vectors sampled across active river basins and flood plains. It fuses Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) backscatter with HydroSHEDS elevation, hydrographic flow networks, ECMWF GEOGloWS river discharge time-series, and meteorological rainfall indices.
This repository provides pre-compiled, memory-mapped NumPy binary arrays (features_80m.npy and labels_80m.npy) alongside calibration databases (sentinel_v7_tactical.db), mission logs, and normalization scalers (scaler.joblib).
Dataset Structure & Files
| File Name | Footprint | Format | Description |
|---|---|---|---|
features_80m.npy |
2.56 GB | NumPy Array (float32) |
Memory-mapped matrix of shape (83986875, 8) containing the 8 core hydrological features |
labels_80m.npy |
320.38 MB | NumPy Array (float32) |
Ground truth flood risk indicators of shape (83986875,) |
scaler.joblib |
1.3 KB | Scikit-Learn Joblib | Fitted MinMaxScaler mapping raw physical inputs to [0.0, 1.0] |
sentinel_v7_tactical.db |
26.15 MB | SQLite Database | Relational database containing municipal ward topography and telemetry |
localities.json |
480 KB | JSON | Spatial coordinates and metadata for monitored urban nodes |
lucknow_flood_hist_19_21.csv |
20 KB | CSV | Ground-truth historical monsoon high-water marks (2019-2021) |
monte_carlo_stress_log.csv |
7.88 MB | CSV | Epistemic stress testing and parameter sensitivity audit logs |
The 8 Telemetry Features in features_80m.npy
Shape: (83,986,875 samples, 8 features)
Dtype: float32
| Index | Feature Key | Unit | Description |
|---|---|---|---|
| 0 | elevation |
meters | Topographic elevation above sea level (HydroSHEDS DEM) |
| 1 | river_dist |
km | Proximity to nearest primary river or drainage artery |
| 2 | rainfall_mm |
mm | Instantaneous precipitation intensity |
| 3 | runoff_mm |
mm | Modeled surface runoff accumulation volume |
| 4 | soil_moisture |
fraction [0-1] | Soil saturation percentage |
| 5 | river_discharge |
$\text{m}^3/\text{s}$ | Upstream volumetric discharge ($Q$) from ECMWF GEOGloWS |
| 6 | pop_density |
persons/$\text{km}^2$ | Demographic exposure proxy |
| 7 | sar_vh |
dB | Sentinel-1 SAR IW GRD cross-polarization backscatter |
Quickstart: Memory-Mapped Loading in Python
Because features_80m.npy is pre-formatted as a contiguous C-order binary matrix, you can stream batches without loading the entire 2.56 GB into system RAM:
from huggingface_hub import hf_hub_download
import numpy as np
import joblib
# 1. Download binary feature array and scaler
feat_path = hf_hub_download(repo_id="abdullahashraf122/lucknow_hufp_datasets", filename="features_80m.npy")
label_path = hf_hub_download(repo_id="abdullahashraf122/lucknow_hufp_datasets", filename="labels_80m.npy")
scaler_path = hf_hub_download(repo_id="abdullahashraf122/lucknow_hufp_datasets", filename="scaler.joblib")
# 2. Open via Memory-Mapping (zero RAM overhead)
X_mmap = np.load(feat_path, mmap_mode="r")
y_mmap = np.load(label_path, mmap_mode="r")
print(f"Features Shape: {X_mmap.shape} (dtype: {X_mmap.dtype})")
print(f"Labels Shape: {y_mmap.shape} (dtype: {y_mmap.dtype})")
# 3. Stream a batch of 10,000 vectors
batch_X = X_mmap[:10000]
batch_y = y_mmap[:10000]
print(f"Sample Feature Vector (First Record): {batch_X[0]}")
Data Curation & Provenance
- SAR Radiometric Calibration: Copernicus Sentinel-1 Interferometric Wide Swath (IW) Ground Range Detected (GRD) products calibrated to $\gamma^0 / \sigma^0$ backscatter.
- Hydrographic Network: HydroSHEDS 15-arcsecond river network vectors.
- Hydrological Streamflow: Global Flood Awareness System (GloFAS) and GEOGloWS ECMWF reanalysis.
License & Attribution
Distributed under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license.
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