Paper Citation Record · LEDGER

A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning

As of 22 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.

pith.paper-citation-record.v1
1908.03438 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:16:47.767777Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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
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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: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:48.078610Z

Source-reported events for the cited work

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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: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:48.058586Z

Source-reported events for the cited work

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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

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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

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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

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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

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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

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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

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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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:16:47.877593Z

Source-reported events for the cited work

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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

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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

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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 2026-08-22T06:32:14.747728+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.