Pith. sign in

Paper Citation Record · LEDGER

Unsupervised Feature Learning in Remote Sensing

As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:1908.02877.

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

pith.paper-citation-record.v1
1908.02877 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:35:25.856954Z

measured 15 of 15 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

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved3
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c50da6c-4a24-4d1f-b881-1a63fc3dc13c · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Unsupervised Feature Learning in Remote Sensing Imagenet classification with deep convolutional neural networks,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.115253Z

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.

source=pdf_text observed=2026-08-14T14:35:25.786475Z digest=sha256:9bb1006223af58f9e97ca52ac84580e7fbe7c328b0d825a8ea75711592dde2d7

Observation c58a8175-b2d5-41a2-87a3-a8f6f82b82bc · outbound

This paper cites ImageNet: A large-scale hierarchical image database,.

Unsupervised Feature Learning in Remote Sensing ImageNet: A large-scale hierarchical image database,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.099637Z

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.

source=pdf_text observed=2026-08-14T14:35:25.791950Z digest=sha256:4110bbc6889afae52dd5b4d04a8ae8deccab104b229ff93e0bdcfb09df6514e0

Observation 98580f2a-1a23-435f-a091-69a909653715 · outbound

This paper cites Unsupervised feature learning via non-parametric instance dis- crimination,.

Unsupervised Feature Learning in Remote Sensing Unsupervised feature learning via non-parametric instance dis- crimination,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.083990Z

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.

source=pdf_text observed=2026-08-14T14:35:25.796721Z digest=sha256:f1db819617cccfb4d323f1588a4beef84dcc2bd711dec50b197d8e7c43d2a1ae

Observation ee4645ae-af0d-4d3e-9a43-cf156be659c8 · outbound

This paper cites xView: Objects in Context in Overhead Imagery.

Unsupervised Feature Learning in Remote Sensing xView: Objects in Context in Overhead Imagery

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-14T14:35:25.801561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:35:25.801561Z digest=sha256:43a708f8e3038cf3e6c920cad79c3301795932d283e62ea198e6f519c3aa3415

Observation 61030a59-b940-4464-9892-4c48861391be · outbound

This paper cites DIUx xView 2018 detection challenge.

Unsupervised Feature Learning in Remote Sensing DIUx xView 2018 detection challenge

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.069234Z

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.

source=pdf_text observed=2026-08-14T14:35:25.806739Z digest=sha256:d6ec14dcd6bedd48d4e36696beb9cfce3950eff43163b1679c8a60a1d82e8636

Observation 9abb6645-f3cf-4683-907c-07e7021b410c · outbound

This paper cites Microsoft COCO: Common objects in context,.

Unsupervised Feature Learning in Remote Sensing Microsoft COCO: Common objects in context,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.053921Z

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.

source=pdf_text observed=2026-08-14T14:35:25.811768Z digest=sha256:92cf4f21b5874e2aa545c0a1adaeaa23503346edb78a49e9c9d0bc3aa975be0e

Observation 9c96a8af-ece6-4ec1-8c3a-235e3334997a · outbound

This paper cites Reduced Focal Loss: 1st Place Solution to xView object detection in Satellite Imagery.

Unsupervised Feature Learning in Remote Sensing Reduced Focal Loss: 1st Place Solution to xView object detection in Satellite Imagery

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-14T14:35:25.817978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:35:25.817978Z digest=sha256:c101a0c423b06ec316bfbe1cb30e685f2e28100ff24e2fa61c9dbf5b03cfcac5

Observation f81f5210-7dd2-4d28-b346-591bbb425e90 · outbound

This paper cites Focal loss for dense object detection,.

Unsupervised Feature Learning in Remote Sensing Focal loss for dense object detection,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.038503Z

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.

source=pdf_text observed=2026-08-14T14:35:25.822782Z digest=sha256:c6b437c7073675ab4dd0550278e2d92c6d79884471da76f746eaf67b9f504e3b

Observation e940c627-ed4a-4df7-9c53-17b3c0376134 · outbound

This paper cites Non-contrastive estimation: A new estimation principle for unnormalized statistical models,.

Unsupervised Feature Learning in Remote Sensing Non-contrastive estimation: A new estimation principle for unnormalized statistical models,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.022125Z

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.

source=pdf_text observed=2026-08-14T14:35:25.827513Z digest=sha256:8bbe7df9a4c7eb200d39912a6942aa6b26961927a7c0286585301bb182cf1762

Observation 19b6a8c4-80df-4319-9135-1c6d69e45742 · outbound

This paper cites Recent Advance in Content-based Image Retrieval: A Literature Survey.

Unsupervised Feature Learning in Remote Sensing Recent Advance in Content-based Image Retrieval: A Literature Survey

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-14T14:35:25.832037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:35:25.832037Z digest=sha256:910a253e385ebf21c1773b41f0a4610d4cc85b1596c971e6fd57b2a6b2c725fa

Observation 4247c5be-b669-47a1-a793-46112aea3e04 · outbound

This paper cites Bag-of-visual-words and spatial extensions for land-use classification,.

Unsupervised Feature Learning in Remote Sensing Bag-of-visual-words and spatial extensions for land-use classification,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:26.004724Z

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.

source=pdf_text observed=2026-08-14T14:35:25.837192Z digest=sha256:949072e1f852dab55e9e40d85f84928fe3d1d5c44350f101b3a5cf7deaceabd5

Observation 54c083ee-ef70-4d28-bf80-66d7320f3924 · outbound

This paper cites UC Merced land use dataset.

Unsupervised Feature Learning in Remote Sensing UC Merced land use dataset

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:25.989467Z

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.

source=pdf_text observed=2026-08-14T14:35:25.842521Z digest=sha256:f6416acbc238200273e4bac5937c88b3ea8c3861caf9610530c6766f22b15f04

Observation 3708a63b-3bb5-46cb-b364-48a2d7d303c1 · outbound

This paper cites Visualizing data using t-SNE,.

Unsupervised Feature Learning in Remote Sensing Visualizing data using t-SNE,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:25.974259Z

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.

source=pdf_text observed=2026-08-14T14:35:25.847328Z digest=sha256:e62c840d43c74f023162634dd1c1dcd199b3c63fd0213c04f21911ebbc929ffe

Observation fca245ee-8c15-4c6a-ad2e-cfd927d62d19 · outbound

This paper cites Fellbaum, WordNet: An Electronic Lexical Database , Bradford Books, 1998.

Unsupervised Feature Learning in Remote Sensing Fellbaum, WordNet: An Electronic Lexical Database , Bradford Books, 1998

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:35:25.958969Z

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.

source=pdf_text observed=2026-08-14T14:35:25.852179Z digest=sha256:37acb947b6d269ef08f1ac62606c2567f07a5bf8d66c0e0557287d5a96846cdf

Observation 7ac2ab5c-f98c-4492-9844-1cdc0b44b783 · outbound

This paper cites Improving performance of multiclass classification by inducing class hierarchies,.

Unsupervised Feature Learning in Remote Sensing Improving performance of multiclass classification by inducing class hierarchies,

Reference 15

Resolution
malformed identifier
raw_fallback, observed 2026-08-14T14:35:25.942820Z

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.

source=pdf_text observed=2026-08-14T14:35:25.856954Z digest=sha256:ea06e5024d13f8cccd8915f6609f2aa286cac2c68845b8a2e30bb253c4ed90e8

Pith citing papers

No inbound Pith citation observations are available.