Paper Citation Record · LEDGER

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images

As of 21 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.11799.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.11799 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:09:29.237803Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

29 of 29 outbound references displayed

verified exact2
verified fuzzy16
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d687400e-ef68-4fe0-aeb7-aa4abfd9f3a2 · outbound

This paper cites Semantic segmenta- tion of small objects and modeling of uncertainty in urban remote sens- ing images using deep convolutional neural networks,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Semantic segmenta- tion of small objects and modeling of uncertainty in urban remote sens- ing images using deep convolutional neural networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.758971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f1caaccf-bdec-4729-8a1e-811dcd79fb23 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Fully convolutional networks for semantic segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.743029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 946e6717-84b3-4cf6-9174-f8a2e3de0240 · outbound

This paper cites Pyramid Scene Parsing Network.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Pyramid Scene Parsing Network

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.095993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddb25e09-ae9f-4cb7-b1d1-ff20f798007d · outbound

This paper cites Understanding Convolution for Semantic Segmentation.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Understanding Convolution for Semantic Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.102437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f91125c8-f156-48ca-802e-6d6cabbf53d9 · outbound

This paper cites Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.108035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44f23c9b-ca04-415f-bcbb-393443a988d7 · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Multi-Scale Context Aggregation by Dilated Convolutions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.113859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbabf9b4-355f-4536-ac41-c9fe32494c78 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.725262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 03f989d4-2a8c-48df-8eb2-c6cfe7ce754f · outbound

This paper cites A mixed-scale dense convolutional neural network for image analysis,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images A mixed-scale dense convolutional neural network for image analysis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.708558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4ae1cb1b-0f30-4806-a149-269f1c637f13 · outbound

This paper cites Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.691433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 947a1ff1-89f0-4700-8ddd-c3d06ac6d10f · outbound

This paper cites Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.675129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8cb74697-3930-4fe3-bfb9-e03e1e6dda2c · outbound

This paper cites Delving deep into rectiﬁers: Surpassing human-level performance on imagenet classiﬁcation,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Delving deep into rectiﬁers: Surpassing human-level performance on imagenet classiﬁcation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.660018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7da9e86d-60d5-4d15-9292-60baf732bc00 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.145423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3603ee47-d35a-406b-bf95-7c2926adb4a6 · outbound

This paper cites Deep residual learning for image recognition,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Deep residual learning for image recognition,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.150791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccefc96b-d5d9-4489-86e4-d6213bfc4ec5 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images ImageNet Large Scale Visual Recognition Challenge,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.155455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3c85a35-83ad-4a98-8350-eea8818a45bc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Adam: A Method for Stochastic Optimization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.159990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1217e48e-f8bf-441c-ba7b-a65e9c396463 · outbound

This paper cites On the convergence of adam and beyond,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images On the convergence of adam and beyond,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.624087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 05f7b73b-711a-4a77-991b-37652d41bf77 · outbound

This paper cites 2D Semantic Labeling Contest.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images 2D Semantic Labeling Contest

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.608308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 90e7484d-99b0-4c84-9084-e8b2e26692b1 · outbound

This paper cites A comparison of deep learning architectures for semantic mapping of very high resolution images,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images A comparison of deep learning architectures for semantic mapping of very high resolution images,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.592679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4c987c6e-b9c9-435e-855b-1239db066b25 · outbound

This paper cites Joint learning from earth observation and openstreetmap data to get faster better semantic maps,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Joint learning from earth observation and openstreetmap data to get faster better semantic maps,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.575525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 928d7f06-ef75-4fd7-8202-e2e3c0bcf0c2 · outbound

This paper cites Gated convo- lutional neural network for semantic segmentation in high-resolution images,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Gated convo- lutional neural network for semantic segmentation in high-resolution images,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.556223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fea1e0d8-36a9-436f-8c77-125116e78bc7 · outbound

This paper cites Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.191176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cf0a015-23be-482a-848c-dd0fa6692fc2 · outbound

This paper cites Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:09:29.327868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 14974f8b-48d7-4ab2-b6c9-57fbd84a8791 · outbound

This paper cites RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:09:29.303922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5e141a17-4d79-4a20-a336-29c3b63dc340 · outbound

This paper cites Urban land cover classiﬁcation with missing data modalities using deep convolutional neural networks,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Urban land cover classiﬁcation with missing data modalities using deep convolutional neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.539543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71bb4bb1-ae5d-498c-a80f-bb3b80f2c736 · outbound

This paper cites Efﬁcient piecewise training of deep structured models for semantic segmentation,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Efﬁcient piecewise training of deep structured models for semantic segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.522890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e6da46ea-db6b-49bc-af20-b19474b8721f · outbound

This paper cites Effective semantic pixel labelling with convolutional networks and conditional random ﬁelds,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Effective semantic pixel labelling with convolutional networks and conditional random ﬁelds,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.503253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 32c156ac-b673-4c85-80d3-e968cbc6c3eb · outbound

This paper cites Semantic segmentation of earth observation data using multimodal and multi-scale deep networks,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Semantic segmentation of earth observation data using multimodal and multi-scale deep networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:29.480717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d6a0ba4c-fffb-46a6-b91f-4221a0ed2c31 · outbound

This paper cites U-net: Convolutional net- works for biomedical image segmentation,.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images U-net: Convolutional net- works for biomedical image segmentation,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.232010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4230019b-cbfa-4dcb-9466-0d2f10e7ddc0 · outbound

This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T10:09:29.237803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.