Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:16:47.767777Z
Paper Citation Record · LEDGER
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning
As of 16 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:1908.03438.
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 2026-08-16T06:30:59.297886+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
17 of 17 outbound references displayed
- verified exact0
- verified fuzzy14
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 29c85b19-dfb2-4931-a4a0-840a0dfa9a31 · outbound
This paper cites It shows the utilization of land resources and the transformation results of human beings.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning It shows the utilization of land resources and the transformation results of human beings
Reference 1
- Resolution
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- raw_fallback, observed 2026-08-14T14:16:48.078610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f7b32bb8-e019-4e90-83aa-72836c62c101 · outbound
This paper cites Data and Classification System Chinese GF-1 satellite images at the spatial resolution of 8 m in 2017, over the Guangdong Province with the area of 179,700 km 2, are obtained.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Data and Classification System Chinese GF-1 satellite images at the spatial resolution of 8 m in 2017, over the Guangdong Province with the area of 179,700 km 2, are obtained
Reference 2
- Resolution
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- raw_fallback, observed 2026-08-14T14:16:48.058586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 30b81857-b154-47af-be70-237ce8d15159 · outbound
This paper cites Data volume is up to 118 GB.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Data volume is up to 118 GB
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:48.041264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9908e29e-c5bf-4837-9cd9-394849bb7578 · outbound
This paper cites an unresolved cited work.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:16:48.024426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 28f60fdf-bab5-464d-b3cb-e8020380fb13 · outbound
This paper cites Compared with the traditional method, we complete a large -scale land -use classification in a small amount of time.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Compared with the traditional method, we complete a large -scale land -use classification in a small amount of time
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:48.007770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 130746f0-5333-4d9b-a966-e36f0a5cb85d · outbound
This paper cites Using geometrical, textural, and contextual information of land parcels for classifica tion of detailed urban land use,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Using geometrical, textural, and contextual information of land parcels for classifica tion of detailed urban land use,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.991497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f9869f84-05c6-4122-a647-0895f3d48b76 · outbound
This paper cites A spectral-structural bag- of-features scene classifier for very high spatial re solution remote sensing imagery,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning A spectral-structural bag- of-features scene classifier for very high spatial re solution remote sensing imagery,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.974827Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 56cabe37-5cb4-48cb-bd6f-4628d0787865 · outbound
This paper cites Geographic object -based image analysis-towards a new paradigm,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Geographic object -based image analysis-towards a new paradigm,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.959308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d577e3d8-6811-410d-9fea-2f5f89c81375 · outbound
This paper cites Object- based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Object- based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.943356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e41c205d-e5fa-4a18-87af-da56fadb02c8 · outbound
This paper cites Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.926099Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 59093473-29d6-450c-81e9-450b4067c73b · outbound
This paper cites Automated urban land-use classification with remote sensing,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Automated urban land-use classification with remote sensing,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.909351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 130a5217-faa5-4acf-aaa1-c39937918aa7 · outbound
This paper cites Urban land- use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery ,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Urban land- use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery ,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.893579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e762cebc-5e11-48f9-ac4f-8005788258ef · outbound
This paper cites Imagenet classification with deep convolutional neural networ ks,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Imagenet classification with deep convolutional neural networ ks,
Reference 13
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- raw_fallback, observed 2026-08-14T14:16:47.877593Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 36c609ae-34df-44c5-a4ef-676b304d1d0c · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning U-net: Convolutional networks for biomedical image segmentation,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.861375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e534ee3a-d77a-4055-91db-30e3e8441c21 · outbound
This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:16:47.757567Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d38f39a-5db2-4b9a-98d2-8809109896bf · outbound
This paper cites Pyramid scene parsing network,.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Pyramid scene parsing network,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:16:47.844628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8b2713c7-0588-4fec-90b1-f7e49d331f48 · outbound
This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:16:47.767777Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.