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Crater Detection and Population Statistics in Tianwen-1 Landing Area Based on Segment Anything Model (SAM)

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-94-017-1035-0_6openalex-snapshot

Cratering Chronology and the Evolution of Mars

inferredcites →https://doi.org/10.1007/s11214-021-00823-wopenalex-snapshot

High Resolution Imaging Camera (HiRIC) on China’s First Mars Exploration Tianwen-1 Mission

inferredcites →https://doi.org/10.1016/j.epsl.2009.12.041openalex-snapshot

Planetary surface dating from crater size–frequency distribution measurements: Partial resurfacing events and statistical age uncertainty

inferredcites →https://doi.org/10.1016/j.epsl.2020.116272openalex-snapshot

A catalogue of impact craters larger than 200 m and surface age analysis in the Chang'e-5 landing area

inferredcites →https://doi.org/10.1016/j.epsl.2022.117764openalex-snapshot

Transverse aeolian ridges in the landing area of the Tianwen-1 Zhurong rover on Utopia Planitia, Mars

inferredcites →https://doi.org/10.1016/j.icarus.2013.09.018openalex-snapshot

Mud volcanism and morphology of impact craters in Utopia Planitia on Mars: Evidence for the ancient ocean

inferredcites →https://doi.org/10.1016/j.icarus.2018.06.022openalex-snapshot

Lunar crater identification via deep learning

inferredcites →https://doi.org/10.1016/j.icarus.2021.114657openalex-snapshot

Geological characteristics of China's Tianwen-1 landing site at Utopia Planitia, Mars

inferredcites →https://doi.org/10.1016/j.icarus.2023.115776openalex-snapshot

Cross-attention induced multilayer domain adaptation network for extraction of sub-kilometer craters from HiRIC images

inferredcites →https://doi.org/10.1016/j.icarus.2023.115797openalex-snapshot

A flexible deep learning crater detection scheme using Segment Anything Model (SAM)

inferredcites →https://doi.org/10.1016/j.isprsjprs.2024.01.004openalex-snapshot

ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning

inferredcites →https://doi.org/10.1016/j.media.2023.102918openalex-snapshot

Segment anything model for medical image analysis: An experimental study

inferredcites →https://doi.org/10.1016/j.pss.2017.05.006openalex-snapshot

Crater density differences: Exploring regional resurfacing, secondary crater populations, and crater saturation equilibrium on the moon

inferredcites →https://doi.org/10.1016/j.pss.2019.03.008openalex-snapshot

Automated crater detection on Mars using deep learning

inferredcites →https://doi.org/10.1016/s0262-8856(00)00111-6openalex-snapshot

Crater detection for autonomous landing on asteroids

inferredcites →https://doi.org/10.1016/s0734-189x(88)80033-1openalex-snapshot

A survey of the hough transform

inferredcites →https://doi.org/10.1023/a:1011941121102openalex-snapshot

Mars/Moon Cratering Rate Ratio Estimates

inferredcites →https://doi.org/10.1029/2011je003966openalex-snapshot

A new global database of Mars impact craters ≥1 km: 1. Database creation, properties, and parameters

inferredcites →https://doi.org/10.1029/2018je005820openalex-snapshot

Rock Abundance and Crater Density in the Candidate Chang'E‐5 Landing Region on the Moon

inferredcites →https://doi.org/10.1029/2021ea001670openalex-snapshot

Characterization of the Candidate Landing Region for Tianwen‐1—China's First Mission to Mars

inferredcites →https://doi.org/10.1029/2021gl094903openalex-snapshot

Geological Characteristics and Targets of High Scientific Interest in the Zhurong Landing Region on Mars

inferredcites →https://doi.org/10.1029/2021je007089openalex-snapshot

Layered Ejecta Craters in the Candidate Landing Areas of China's First Mars Mission (Tianwen‐1): Implications for Subsurface Volatile Concentrations

inferredcites →https://doi.org/10.1029/2021je007137openalex-snapshot

Landing Site Selection and Characterization of Tianwen‐1 (Zhurong Rover) on Mars

inferredcites →https://doi.org/10.1029/93je00121openalex-snapshot

Degradation of selected terrestrial and Martian impact craters

inferredcites →https://doi.org/10.1038/s41467-020-20215-yopenalex-snapshot

Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

inferredcites →https://doi.org/10.1038/s41550-020-1148-6openalex-snapshot

China’s first mission to Mars

inferredcites →https://doi.org/10.1109/access.2021.3066445openalex-snapshot

Moon Impact Crater Detection Using Nested Attention Mechanism Based UNet++

inferredcites →https://doi.org/10.1109/iccv51070.2023.00371openalex-snapshot

Segment Anything

inferredcites →https://doi.org/10.1109/icip.2014.7025316openalex-snapshot

Automated crater detection and counting using the hough transform

inferredcites →https://doi.org/10.1109/jstars.2023.3314128openalex-snapshot

SqUNet: An High-Performance Network for Crater Detection With DEM Data

inferredcites →https://doi.org/10.1109/tgrs.2021.3100137openalex-snapshot

Multiscale CNN Based on Component Analysis for SAR ATR

inferredcites →https://doi.org/10.1109/tgrs.2024.3356074openalex-snapshot

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation Based on Visual Foundation Model

inferredcites →https://doi.org/10.1126/science.1195050openalex-snapshot

Global Distribution of Large Lunar Craters: Implications for Resurfacing and Impactor Populations

inferredcites →https://doi.org/10.26464/epp2023005openalex-snapshot

Observations and interpretations of geomorphologic features in the Tianwen-1 landing area on Mars by using orbital imagery data

inferredcites →https://doi.org/10.3133/sim3292openalex-snapshot

Geologic map of Mars

inferredcites →https://doi.org/10.3390/rs12172694openalex-snapshot

An Effective Lunar Crater Recognition Algorithm Based on Convolutional Neural Network

inferredcites →https://doi.org/10.3390/rs14153608openalex-snapshot

Population of Degrading Small Impact Craters in the Chang’E-4 Landing Area Using Descent and Ground Images

inferredcites →https://doi.org/10.3390/rs15010266openalex-snapshot

MC-UNet: Martian Crater Segmentation at Semantic and Instance Levels Using U-Net-Based Convolutional Neural Network

inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.5373217openalex-extracted

Parameter-Efficient Fine-Tuning of the Segment Anything Model for Remotesensing Sar Flood Mapping

inferred← citeshttps://doi.org/10.1049/stg2.12199openalex-snapshot

Image processing‐based noise‐resilient insulator defect detection using YOLOv8x

inferred← citeshttps://doi.org/10.1109/jstars.2025.3597739openalex-snapshot

A Novel Flexible Architecture Based on SAM for Automatic Extraction of Rampart Craters From Martian High-Resolution Images

inferred← citeshttps://doi.org/10.1109/lgrs.2026.3693662openalex-snapshot

Self-Improving SAM With Specialist Knowledge via Adaptive Direct Preference Optimization for Crater Segmentation

inferred← citeshttps://doi.org/10.1109/missf68264.2026.11522101openalex-snapshot

Machine Vision Approaches to Mars Rover Image Analysis

inferred← citeshttps://doi.org/10.3390/rs17183139openalex-snapshot

Crater-MASN: A Multi-Scale Adaptive Semantic Network for Efficient Crater Detection

inferred← citeshttps://doi.org/10.61977/ati2024068openalex-snapshot

A review of deep learning-based analyses of impact crater detection on different celestial bodies

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