https://www.patsnap.com/resources/blog/rd-blog/semantic-segmentation-and-scene-understanding-patent-landscape/ · Patsnap · data cut-off 2026-07-31 · downloaded from the live page
Patent Landscape · AI & Computer Vision
Semantic Segmentation and Scene Understanding Patents
  • Filing activity peaked in 2023 at 144 families, then declined — the growth phase for encoder-decoder and boundary-refinement claims looks to be behind, not ahead of, current filers.
  • China accounts for 304 of the tracked filings versus 195 from the United States so freedom-to-operate work that only screens USPTO/EPO records misses the largest single filing base.
  • None of the six highest-momentum assignees filed in the latest tracked year a signal that the field's leaders may be consolidating existing claims rather than actively extending them right now.
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647
Published Records
14%
Top-5 Share of All Records
+141%
Filing Growth 2021→2024
CN
Leading Jurisdiction

Filing growth compares 2021 (41 records) with 2024 (99) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 647 records in scope (CR5), not by the ranked leaders only.

Published byPatsnap Research··8 min readSourced from Patsnap Eureka
Overview

What this landscape covers

This dataset tracks 647 patent families filed against semantic segmentation, scene understanding and instance segmentation, narrowed to filings whose claims or descriptions touch encoder-decoder architectures, receptive field design, boundary refinement, annotation cost reduction, or domain adaptation. The IPC scope centres on G06V10, G06T7 and G06V20 — image and video recognition, image data processing, and scene/object recognition respectively. Coverage runs from 2015 through the 2026 data cut-off, though the most recent year is necessarily undercounted: publication typically lags filing by around 18 months, so 2026 and much of 2025 will fill in as records publish.

The composition skews toward core computer-vision IPC classes rather than adjacent hardware: G06V and G06N dominate, with G06T close behind, while control (G05D), radar/positioning (G01S) and optics (G02B) each carry a small minority of records. That pattern says the claim activity here is concentrated in software-side recognition and processing methods, not in the sensor or control hardware that consumes their output.

Filing activity, 2017–2026
  1. 1ADOBE INC26
  2. 2MAGIC LEAP INC19
  3. 3ZHEJIANG UNIV16
  4. 4SAMSUNG ELECTRONICS CO LTD15
  5. 5ZOOX INC14
  6. 6INTEL CORP14
  7. 7ROBERT BOSCH GMBH13
  8. 8TENCENT TECHNOLOGY (SHENZHEN) CO LTD12
  9. 9CREATEAI INC12
  10. 10PROMATON HLDG BV11
Source: Patsnap Eureka. Assignee ranking and totals. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP

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The Numbers

Filing trend and technology composition

Two views of the same 647-family dataset: how filing volume has moved year over year, and how the underlying IPC classifications distribute across the classification scheme.

A filing peak already behind the field

Filings rose from 21 in 2017 to a peak of 144 in 2023, with the 2022 midpoint at 102 — growth that has since flattened or reversed. Because publication lags filing by roughly 18 months, the apparent 2026 drop-off to 13 partly reflects records not yet published rather than a hard stop in filing activity; even allowing for that lag, the shape shows a matured filing wave rather than an accelerating one.

A filing peak already behind the field0387511315021201720182019202020212022144202320242025132026Most recent year is partial — publication lag means later filings are not yet visible.

Recognition and processing classes dominate

G06V (543 records) and G06N (395) lead the IPC composition, with G06T (301) close behind; G06K, G06F and the smaller hardware-adjacent classes (G05D, G01S, G02B) each account for a modest share. The concentration in recognition and AI-model classes over control or optics classes indicates most claim activity sits in software methods rather than the physical systems that deploy them.

Recognition and processing classes dominateG06V · Image/video recognition54383.9%G06N · Computing based on AI models39561.1%G06T · Image data processing & genera…30146.5%G06K · Data recognition & presentation13420.7%G06F · Electric digital data processi…7010.8%G05D · Control of non-electric variab…213.2%G01S · Radar, sonar & positioning162.5%G02B · Optical elements & systems162.5%Other10516.2%

Shares are the percentage of the 647 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.

Source: Patsnap Eureka. Filing trend and technology composition. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.

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Key Patents

The records other filings cite most

Representative Filing
US20240331165A12024-10-03

Cross-domain remote sensing image semantic segmentation method based on iterative intra-domain adaptation and self-training

ZHEJIANG UNIVERSITY

Filed by Zhejiang University, this record describes a two-stage domain-adaptation pipeline: first reducing inter-domain differences between a labelled source domain and an unlabelled target domain, then running an iterative intra-domain adaptation step that sorts pseudo-label segmentation results by confidence and retrains on the most credible subset. The method targets remote-sensing imagery specifically, where labelled data is scarce and domain shift between sensors or geographies is common.Published 2024-10-03 — illustrates the annotation-cost and domain-adaptation branch of this landscape rather than defining its outer boundary.

US20240331165A1 — patent drawing 1US20240331165A1 — patent drawing 2
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Most-cited records in this corpus
#Publication no.Patent titleCitations
1US20190057507A1System and method for semantic segmentation of images356
2AU2020103901A4Image Semantic Segmentation Method Based on Deep Full Convolutional Network and Conditional Random Field266
3US20210089807A1System and method for boundary aware semantic segmentation216
4US20180053056A1Augmented reality display device with deep learning sensors193
5US20190108639A1Systems and Methods for Semantic Segmentation of 3D Point Clouds192
6US20170262735A1Training constrained deconvolutional networks for road scene semantic segmentation158
7US20180260956A1System and method for semantic segmentation using hybrid dilated convolution (HDC)154
8US20190147582A1Adversarial learning of photorealistic post-processing of simulation with privileged information146
9US9953236B1System and method for semantic segmentation using dense upsampling convolution (DUC)146
10US20160358337A1Image semantic segmentation146

Citation counts inside a searched corpus favour older, earlier-published records — treat this as a signal of influence on the field's terminology and framing, not of current commercial relevance.

Each row carries its publication number; clicking a row searches Eureka by that number.

Source: Patsnap Eureka. Citation counts and representative records. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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Signals

What the filing pattern actually tells you

Read together, the trend, the geography and the citation table point to a field whose foundational claims are largely staked, with activity now concentrated in refinement and domain-specific adaptation.

Filing Trajectory
144 in 2023
peak filing year

The growth phase has passed its peak

Filings rose steadily from 21 in 2017 to 144 in 2023 before declining. Combined with the 18-month publication lag, this suggests the core architectural claims — encoder-decoder variants, receptive-field tuning — were staked out earlier in the window, and later filings increasingly target refinements or domain-specific applications.

Trend data, 2017–2026
Filing Geography
304 China vs 195 US
leading receiving offices

China is the largest single filing base

China's receiving office accounts for 304 records against 195 for the United States, with Europe, WIPO/PCT, Australia and India trailing well behind. Any clearance or monitoring exercise that stops at USPTO and EPO records is screening against the smaller of the two largest filing pools.

Receiving-office counts, this dataset
Citation Concentration
356 citations
top-cited record

Early US filings still anchor the terminology

The most-cited record in this corpus, a US filing on semantic segmentation of images, carries 356 citations — well ahead of the next entries. High citation counts here reflect early publication and broad terminology, not necessarily current enforceability or commercial weight.

Most-cited records table
Collaboration Density
8 co-assignee pairs
joint-filing links

Collaboration is thin and concentrated

Only eight co-assignee pairs appear across the dataset, and the strongest link — a joint industry-university pairing — recurs just three times. Co-development is the exception here, not the norm; most families trace to a single assignee.

Co-assignee pair analysis
Eureka AI Agent
Looking for what nobody has claimed yet?

Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to semantic segmentation and scene understanding, with the prior art for and against each one.

Find the white space →
Source: Patsnap Eureka. Co-assignee relationships and derived observations. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
Players

Who is filing, and where activity has cooled

The assignee list spans large electronics manufacturers, autonomous-vehicle specialists and Chinese universities, but the momentum data suggests a pause across the highest-activity names rather than a single leader pulling further ahead.

Momentum Signal
0 in latest year
across six leading assignees

No fresh filings from the top names in the latest tracked year

Every one of the six assignees with the strongest recent-year momentum recorded zero filings in the most recent year captured. Given the 18-month publication lag this likely overstates a genuine slowdown, but it is consistent with a field where the leading players are consolidating rather than expanding their claim positions.

Recent-year momentum data
Filer Mix
Universities + industry
assignee composition

Chinese universities sit alongside global electronics and AV firms

The assignee base mixes university filers such as Zhejiang University with global electronics names and autonomous-vehicle-focused entities. That mix suggests both applied commercial development and continued academic-side foundational work are still active, even where commercial filing has slowed.

Assignee ranking, 647 families
Collaboration Pattern
3 joint filings
strongest co-assignee pair

Industry-university pairing is the closest thing to a hub

The strongest co-assignee link in the dataset pairs a German automotive-technology firm with a Chinese university, recurring three times — modest in absolute terms but notably more frequent than any other pairing. It points to at least one active, structured industry-academic channel rather than purely incidental co-filing.

Co-assignee pair analysis
🔍
Under-claimed branches worth scouting
Sub-areas within this scope that show thinner filing density relative to the core encoder-decoder and boundary-refinement claims.
Cross-sensor domain adaptationPoint-cloud instance boundary refinementAnnotation-cost reduction via pseudo-labellingReceptive-field tuning for small-object recallRemote-sensing cross-domain segmentation
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Recent-year filing momentum by assignee
AssigneeRecent yearYoY
Adobe Inc.0
Magic Leap, Inc.0
Zhejiang University0
Samsung Electronics Co., Ltd. (South Korea)0
Zoox, Inc.0
Intel Corporation0
Robert Bosch GmbH (Germany)0
Tencent Technology (Shenzhen) Co., Ltd.0
Source: Patsnap Eureka. Assignee-level momentum. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
What's Next

Where to take this analysis

The dataset points to specific next steps depending on whether the goal is clearance, competitive tracking, or identifying open claim space.

Screen China-originated filings directly

With 304 of 647 records receiving in China against 195 in the United States, any freedom-to-operate review limited to USPTO and EPO records is working from the smaller filing pool. Direct screening of Chinese-language filings and their English-language equivalents is necessary for a complete picture.

Explore filings in Eureka →

Watch for the 2025-2026 publication catch-up

Because publication lags filing by roughly 18 months, the apparent decline from the 2023 peak of 144 filings is partly an artefact of records not yet public. Re-checking the 2024-2026 trend in twelve to eighteen months will give a truer read on whether the field is genuinely cooling.

Track filing trends in Eureka →

Probe the under-claimed branches directly

Domain adaptation across sensor types and annotation-cost reduction via pseudo-labelling both show thinner density than core encoder-decoder claims. A targeted claim search in these branches, rather than the crowded core, is more likely to surface genuine white space.

Search white space in Eureka →
Source: Patsnap Eureka. Forward-looking reading of the same dataset. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
FAQ

Common questions on this landscape

Answers are grounded in the same dataset. Derived from a Patsnap search on Semantic Segmentation and Scene Understanding covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP

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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.

Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.

Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.

Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company's registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.