Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:01:41.482674Z
Paper Citation Record · LEDGER
Mapping waterways worldwide with deep learning
As of 13 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2412.00050.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
19 of 19 outbound references displayed
- verified exact6
- verified fuzzy7
- unresolved4
- parse uncertain0
- malformed identifier2
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 07588324-bec7-42cd-a9c9-530be52eaf24 · outbound
This paper cites Data retrieved from https://download.geofabrik.de/.
Mapping waterways worldwide with deep learning Data retrieved from https://download.geofabrik.de/
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.797130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 6efed8f0-add4-4729-926a-336a02d48f66 · outbound
This paper cites TDX-Hydro: Global High-Resolution Hydrography Derived from TanDEM-X.
Mapping waterways worldwide with deep learning TDX-Hydro: Global High-Resolution Hydrography Derived from TanDEM-X
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.783452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation bb5aa0cb-8b5b-49a0-918f-74ca47fedb29 · outbound
This paper cites Deep learning waterways for rural infrastructure development.
Mapping waterways worldwide with deep learning Deep learning waterways for rural infrastructure development
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T14:01:41.603029Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation fe3e6978-d6c7-413f-bef7-c5af1f1790d7 · outbound
This paper cites WaterNet Outputs and Code.
Mapping waterways worldwide with deep learning WaterNet Outputs and Code
Reference 4
- Resolution
- verified exact
- doi, observed 2026-08-12T14:01:41.585567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 817f051f-0c47-4760-b183-22c4386bfb52 · outbound
This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.
Mapping waterways worldwide with deep learning U-Net: Convolutional Networks for Biomedical Image Segmentation
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:01:41.424166Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4295abb8-811d-42fa-8322-39394aec116e · outbound
This paper cites Deep Residual Learning for Image Recognition.
Mapping waterways worldwide with deep learning Deep Residual Learning for Image Recognition
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:01:41.428186Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58eb0350-bd7b-4b50-b611-f8f5d6206f47 · outbound
This paper cites Road Detections.
Mapping waterways worldwide with deep learning Road Detections
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.769675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d300b021-7471-427d-b4b5-1cb681d880cf · outbound
This paper cites Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems.
Mapping waterways worldwide with deep learning Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:01:41.436927Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2d6b8da-7320-44b3-ad0d-573b98bdca5f · outbound
This paper cites Global prevalence of non-perennial rivers and streams.
Mapping waterways worldwide with deep learning Global prevalence of non-perennial rivers and streams
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.757760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0ee24e7e-6087-4afb-adc7-c0c35979421c · outbound
This paper cites Global extent of rivers and streams.
Mapping waterways worldwide with deep learning Global extent of rivers and streams
Reference 10
- Resolution
- verified exact
- doi, observed 2026-08-12T14:01:41.564601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation d85ee9e5-56cd-4637-8017-5794d61aa920 · outbound
This paper cites High-resolution mapping of global surface water and its long-term changes.
Mapping waterways worldwide with deep learning High-resolution mapping of global surface water and its long-term changes
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.744669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation fb61e577-77b3-45ab-82cc-776f244fee9b · outbound
This paper cites Advancing global hydrologic modeling with the GEOGloWS ECMWF streamflow service.
Mapping waterways worldwide with deep learning Advancing global hydrologic modeling with the GEOGloWS ECMWF streamflow service
Reference 12
- Resolution
- verified exact
- doi, observed 2026-08-12T14:01:41.551688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5d2a382c-a263-4097-bfe9-e3b2ddaf4c02 · outbound
This paper cites Ephemeral stream water contributions to United States drainage networks.
Mapping waterways worldwide with deep learning Ephemeral stream water contributions to United States drainage networks
Reference 13
- Resolution
- verified exact
- doi, observed 2026-08-12T14:01:41.538864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ca390585-8bf5-4dfd-8d07-9fbf53561425 · outbound
This paper cites Global prediction of extreme floods in ungauged watersheds.
Mapping waterways worldwide with deep learning Global prediction of extreme floods in ungauged watersheds
Reference 14
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-12T14:01:41.462623Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f02d9e32-9d14-4ea4-a6d1-5ad4b01985be · outbound
This paper cites A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies.
Mapping waterways worldwide with deep learning A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies
Reference 15
- Resolution
- verified exact
- doi, observed 2026-08-12T14:01:41.517198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5d59813c-7957-4329-ba04-15bf2fc0a1bf · outbound
This paper cites Deep learning models for river classification at sub-meter resolutions from multispectral and panchromatic commercial satellite imagery.
Mapping waterways worldwide with deep learning Deep learning models for river classification at sub-meter resolutions from multispectral and panchromatic commercial satellite imagery
Reference 16
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-12T14:01:41.470093Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 66561bf5-69ef-4e10-b40d-e331892eec31 · outbound
This paper cites Catastrophic floods cause mass displacement and humanitarian cri- sis.
Mapping waterways worldwide with deep learning Catastrophic floods cause mass displacement and humanitarian cri- sis
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.731759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a57b0645-8c8a-405e-a7d0-4f1604ade593 · outbound
This paper cites https : / / apps.
Mapping waterways worldwide with deep learning https : / / apps
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T14:01:41.718725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5bc8f50e-e78c-47e1-ab66-68a228b9c867 · outbound
This paper cites Remote Impact Assess- ment of Rural Infrastructure Development.
Mapping waterways worldwide with deep learning Remote Impact Assess- ment of Rural Infrastructure Development
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:01:41.482674Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.