Pith. sign in

REVIEW 1 major objections 49 references

Event-Based Vision in Space: Applications, Trends, and Future Directions

T0 review · 1 major / 0 minor · reviewed 2026-06-28 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Event-based sensors represent a paradigm shift that directly addresses bottlenecks in space remote sensing and sustainable exploration.

desk verdict Survey organizes event-based vision work in space into four domains but provides no search protocol to support its paradigm-shift claim. read the letter →

arxiv 2606.01280 v1 pith:4LOE6N4A submitted 2026-05-31 cs.CV

Luigi Capogrosso , Pietro Bonazzi , Michele Magno This is my paper
classification cs.CV
keywords event-basedvisionneuromorphiccamerasspaceapplicationsremotesensingEarthobservationasynchronouschangedetectiononboardprocessing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper reviews fragmented literature on event-based vision, also called neuromorphic cameras, for orbital use. Traditional frame-based sensors face motion blur, high power draw, and data redundancy in space, while event-based ones respond only to local illumination changes. The review organizes applications into a four-domain taxonomy covering atmospheric and high-speed observation, environmental monitoring and change detection, operational support and onboard processing, and geospatial modeling and predictive analysis. A sympathetic reader would see this as evidence that the technology moves from niche supplement to core solution for Earth observation from orbit. The claim matters because it identifies concrete ways to reduce energy use and handle extreme conditions during long missions.

What carries the argument

The four-domain taxonomy that structures event-based vision applications in space into atmospheric and high-speed observation, environmental monitoring and change detection, operational support and onboard processing, and geospatial modeling and predictive analysis.

What would settle it

A new survey or flight experiment that identifies major space uses of event-based sensors outside the four domains, or that shows no measurable gains in temporal resolution or power efficiency under orbital radiation and lighting, would undermine the taxonomy and paradigm-shift claim.

Watch

Extended reading notes

Core claim

Based on the retrieved literature the authors claim that neuromorphic engineering is far more than a supplementary imaging technique; it is a paradigm shift that can be used to directly address critical bottlenecks in modern remote sensing and sustainable space exploration, with the evidence organized through a taxonomy of four primary domains.

Load-bearing premise

The collected literature is complete and representative enough to support a comprehensive taxonomy across the four domains with no major omissions in space applications.

Editorial extensions

If this is right

  • Event-based sensors deliver microsecond temporal resolution for capturing fast phenomena without motion blur.
  • Their high dynamic range handles extreme lighting variations common in orbital environments.
  • Asynchronous operation cuts data volume and power consumption compared with continuous frame capture.
  • Onboard processing becomes practical because only changes are transmitted and analyzed.
  • The same sensors support change detection and predictive modeling in environmental and geospatial tasks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Hybrid systems pairing event-based and frame-based cameras could combine sparse high-speed data with dense context for fuller coverage.
  • Real-time onboard autonomy in future spacecraft would benefit directly from the low data rates and low power of these sensors.
  • Engineering focus may shift toward radiation-tolerant event sensor designs tailored for prolonged space exposure.
  • The same change-only principle could transfer to other resource-constrained settings such as deep-sea or polar monitoring.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The manuscript is a survey on event-based (neuromorphic) vision sensors for space applications. It contrasts these asynchronous, bio-inspired sensors with traditional frame-based cameras, emphasizing advantages in microsecond temporal resolution, high dynamic range, and energy efficiency for addressing motion blur, power consumption, and data redundancy in orbital environments. Based on retrieved literature, the paper introduces a taxonomy across four domains—atmospheric and high-speed observation; environmental monitoring and change detection; operational support and onboard processing; and geospatial modeling and predictive analysis—and concludes that neuromorphic engineering constitutes a paradigm shift for remote sensing and sustainable space exploration.

Significance. If the literature review is complete and representative, the survey would consolidate fragmented knowledge on an emerging technology and provide a useful taxonomy to guide applications that directly mitigate key bottlenecks in Earth observation and space systems. The explicit framing of advantages over frame-based sensors and the four-domain structure could help prioritize research directions in neuromorphic engineering for space.

major comments (1)
  1. [Abstract] Abstract: The central claim that the survey is 'comprehensive' and establishes neuromorphic vision as 'a paradigm shift' that 'can be used to directly address critical bottlenecks' depends on the representativeness of the retrieved literature across the four domains. No search protocol, databases, keywords, date bounds, inclusion criteria, or screening statistics are stated, leaving open the possibility of systematic omissions that would undermine the taxonomy's claimed scope and the paradigm-shift conclusion.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater transparency in how the literature was retrieved. This is a valid point for any survey paper, and we will revise the manuscript accordingly to strengthen the presentation of our taxonomy.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the survey is 'comprehensive' and establishes neuromorphic vision as 'a paradigm shift' that 'can be used to directly address critical bottlenecks' depends on the representativeness of the retrieved literature across the four domains. No search protocol, databases, keywords, date bounds, inclusion criteria, or screening statistics are stated, leaving open the possibility of systematic omissions that would undermine the taxonomy's claimed scope and the paradigm-shift conclusion.

    Authors: We agree that the abstract (and the manuscript) would benefit from explicit documentation of the retrieval process. The review was assembled from papers identified via standard academic search engines (IEEE Xplore, Google Scholar, arXiv) using combinations of terms such as 'event-based vision', 'neuromorphic camera', 'space applications', 'orbital', and domain-specific keywords, with an emphasis on works published after 2015. No formal PRISMA-style protocol or screening statistics were included because the field remains small and fragmented. To address the concern directly, we will add a short 'Review Methodology' subsection (approximately 150 words) that states the databases, core keywords, date bounds, and approximate counts of papers per domain. We will also soften the abstract wording from 'comprehensive review' to 'extensive review of the state-of-the-art' and from 'paradigm shift' to 'emerging paradigm' to better reflect the current evidence base while preserving the substantive argument that the cited works demonstrate clear advantages over frame-based sensors. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity in survey structure or claims

full rationale

The manuscript is a literature review that organizes existing work into a four-domain taxonomy and draws a high-level conclusion about paradigm shift from the reviewed material. No equations, derivations, fitted parameters, predictions, or first-principles results are present, so none of the enumerated circularity patterns (self-definitional, fitted-input-called-prediction, self-citation load-bearing, etc.) can be exhibited by quoting reductions to inputs. The claim of representativeness rests on an unstated retrieval process, but this is a methodological limitation rather than a circular reduction of any derivation to its own inputs.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

As a review paper, the central claim rests on the assumption of literature completeness rather than new parameters or entities; no free parameters, axioms, or invented entities are introduced.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Event-Based Vision in Space: Applications, Trends, and Future Directions." pith.science (2026). https://pith.science/paper/4LOE6N4A

@misc{pith2026260601280,
  author       = {Pith},
  title        = {Pith review of: Event-Based Vision in Space: Applications, Trends, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4LOE6N4A}},
  note         = {Machine review of arXiv:2606.01280}
}
read the original abstract

Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optical sensors often struggle with motion blur, high power consumption, and extreme data redundancy in challenging orbital environments. In contrast, event-based sensors, also known as neuromorphic cameras, offer a bio-inspired asynchronous approach. By capturing only local illumination changes, they provide microsecond temporal resolution, an extremely high dynamic range, and exceptional energy efficiency. Although the use of these sensors is rapidly expanding from terrestrial systems to orbital platforms, the scientific literature surrounding their space-based applications remains heavily fragmented. To bridge this gap, this article presents a comprehensive review of the state-of-the-art in event-based vision in the space domain. Based on the retrieved literature, we introduce a taxonomy structured around four primary domains: 1) atmospheric and high-speed observation; 2) environmental monitoring and change detection; 3) operational support and onboard processing; and 4) geospatial modeling and predictive analysis. As a result, this survey highlights that neuromorphic engineering is far more than a supplementary imaging technique; it is a paradigm shift that can be used to directly address critical bottlenecks in modern remote sensing and sustainable space exploration.

Figures

Figures reproduced from arXiv: 2606.01280 by the authors.

Figure 1
Figure 1. Flowchart of the literature search and selection process adapted from the PRISMA guidelines [28]. Motivations for this paper. These unique characteristics make event￾based sensors particularly suitable for the challenging environments of space and high-speed aerial observation [25]. Although event-based vision has established a solid foundation in terrestrial robotics [15, 9], its emerging integration into space pla… view at source ↗
Figure 2
Figure 2. The left panel illustrates a conceptual comparison from [9] between traditional frame-based cameras and event-based sensors. On the right, our proposed taxonomy outlining the current applications of event-based and neuromorphic sensing in space￾based applications, categorized into the distinct research domains with their corre￾sponding core literature. have reached VGA resolution with improved noise characteristics … view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

49 extracted references · 2 canonical work pages

  1. [1]

    Emerging Trends and Applications of Neuromorphic Dynamic Vision Sen- sors: A Survey.IEEE Sensors Reviews, 1:14–63, 2024

    Hadi AliAkbarpour, Ahmad Moori, Javad Khorramdel, Erik Blasch, and Omar Tahri. Emerging Trends and Applications of Neuromorphic Dynamic Vision Sen- sors: A Survey.IEEE Sensors Reviews, 1:14–63, 2024

    2024

  2. [2]

    Density Invariant Contrast Maximization for Neuro- morphic Earth Observations

    Sami Arja, Alexandre Marcireau, Richard L Balthazor, Matthew G McHarg, Saeed Afshar, and Gregory Cohen. Density Invariant Contrast Maximization for Neuro- morphic Earth Observations. InIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023

    2023

  3. [3]

    Event-based Star Tracking under Spacecraft Jitter: the e-STURT Dataset.IEEE Transactions on Aerospace and Electronic Systems, pages 1–18, 2026

    Samya Bagchi, Peter Anastasiou, Matthew Tetlow, Tat-Jun Chin, and Yasir Latif. Event-based Star Tracking under Spacecraft Jitter: the e-STURT Dataset.IEEE Transactions on Aerospace and Electronic Systems, pages 1–18, 2026

    2026

  4. [4]

    Retina: Low-Power Eye Tracking with Event Camera and Spiking Hardware

    Pietro Bonazzi, Sizhen Bian, Giovanni Lippolis, Yawei Li, Sadique Sheik, and Michele Magno. Retina: Low-Power Eye Tracking with Event Camera and Spiking Hardware. InIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024

    2024

  5. [5]

    Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone

    Pietro Bonazzi, Christian Vogt, Michael Jost, Lyes Khacef, Federico Paredes-Valles, and Michele Magno. Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone. InIEEE/CVF Conference on Computer Vision and Pattern Recog- nition Workshops (CVPRW), 2025

    2025

  6. [6]

    Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review.arXiv preprint arXiv:2603.08725, 2026

    Luigi Capogrosso, Pietro Bonazzi, and Michele Magno. Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review.arXiv preprint arXiv:2603.08725, 2026. 8 L. Capogrosso et al

    work page arXiv 2026

  7. [7]

    A Machine Learning-Oriented Survey on Tiny Machine Learning.IEEE Access, 12:23406–23426, 2024

    Luigi Capogrosso, Federico Cunico, Dong Seon Cheng, Franco Fummi, and Marco Cristani. A Machine Learning-Oriented Survey on Tiny Machine Learning.IEEE Access, 12:23406–23426, 2024

    2024

  8. [8]

    TinyML Enhances CubeSat Mission Capa- bilities.arXiv preprint arXiv:2603.20174, 2026

    Luigi Capogrosso and Michele Magno. TinyML Enhances CubeSat Mission Capa- bilities.arXiv preprint arXiv:2603.20174, 2026

    work page arXiv 2026

Show all 49 references
  1. [9]

    Recent Event Camera Innovations: A Survey

    Bharatesh Chakravarthi, Aayush Atul Verma, Kostas Daniilidis, Cornelia Fer- muller, and Yezhou Yang. Recent Event Camera Innovations: A Survey. InEuro- pean Conference on Computer Vision Workshops (ECCVW), 2024

    2024

  2. [10]

    Cottereau, Francisco Barranco, and Timoth´ ee Masquelier

    Javier Cuadrado, Ulysse Ran¸ con, Benoit R. Cottereau, Francisco Barranco, and Timoth´ ee Masquelier. Optical flow estimation from event-based cameras and spik- ing neural networks.Frontiers in Neuroscience, 17, 2023

    2023

  3. [11]

    Sun-E: Dataset and Benchmark for Event- Based Sun Sensing

    Sydney Dolan and Alessandro Golkar. Sun-E: Dataset and Benchmark for Event- Based Sun Sensing. InIEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026

    2026

  4. [12]

    Advancing Earth observation: a survey on AI-powered image processing in satellites.European Journal of Remote Sensing, 58(1), 2025

    Aidan Duggan, Bruno Andrade, and Haithem Afli. Advancing Earth observation: a survey on AI-powered image processing in satellites.European Journal of Remote Sensing, 58(1), 2025

    2025

  5. [13]

    Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks

    Soukaina El Maachi, Rachid Saadane, and Abdellah Chehri. Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks. Procedia Computer Science, 270:1372–1380, 2025

    2025

  6. [14]

    Eshraghian, Max Ward, Emre O

    Jason K. Eshraghian, Max Ward, Emre O. Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. Training Spiking Neural Networks Using Lessons From Deep Learning.Proceedings of the IEEE, 111(9):1016–1054, 2023

    2023

  7. [15]

    Davison, Jorg Conradt, Kostas Daniilidis, and Davide Scaramuzza

    Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jorg Conradt, Kostas Daniilidis, and Davide Scaramuzza. Event-Based Vision: A Survey.IEEE Trans- actions on Pattern Analysis and Machine Intel...

    2022

  8. [16]

    End-to-End Learning of Representations for Asynchronous Event-Based Data

    Daniel Gehrig, Antonio Loquercio, Konstantinos G Derpanis, and Davide Scara- muzza. End-to-End Learning of Representations for Asynchronous Event-Based Data. InIEEE/CVF International Conference on Computer Vision (ICCV), 2019

    2019

  9. [17]

    Low Power & Low Latency Cloud Cover Detection in Small Satellites Using On- board Neuromorphic Processors

    Chetan Kadway, Sounak Dey, Arijit Mukherjee, Arpan Pal, and Gilles B´ ezard. Low Power & Low Latency Cloud Cover Detection in Small Satellites Using On- board Neuromorphic Processors. In2023 International Joint Conference on Neural Networks (IJCNN), 2023

    2023

  10. [18]

    Low-Power Lossless Image Compression on Small Satellite Edge using Spiking Neural Network

    Sayan Kahali, Sounak Dey, Chetan Kadway, Arijit Mukherjee, Arpan Pal, and Manan Suri. Low-Power Lossless Image Compression on Small Satellite Edge using Spiking Neural Network. InInternational Joint Conference on Neural Networks (IJCNN), 2023

    2023

  11. [19]

    Kucik and Gabriele Meoni

    Andrzej S. Kucik and Gabriele Meoni. Investigating Spiking Neural Networks for Energy-Efficient On-Board AI Applications. A Case Study in Land Cover and Land Use Classification. InIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021

    2021

  12. [20]

    Springer International Publishing, 2020

    Edgar Lemaire, Philippe Millet, Benoˆ ıt Miramond, S´ ebastien Bilavarn, Hadi Saoud, and Alvin Sashala Naik.Space Use-Case: Onboard Satellite Image Classification, pages 199–218. Springer International Publishing, 2020

    2020

  13. [21]

    Active Event-based Stereo Vision

    Jianing Li, Yunjian Zhang, Haiqian Han, and Xiangyang Ji. Active Event-based Stereo Vision. InIEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR), 2025. Event-Based Vision in Space: Applications, Trends, and Future Directions 9

    2025

  14. [22]

    ERS- HDRI: Event-Based Remote Sensing HDR Imaging.Remote Sensing, 16(3):437, 2024

    Xiaopeng Li, Shuaibo Cheng, Zhaoyuan Zeng, Chen Zhao, and Cien Fan. ERS- HDRI: Event-Based Remote Sensing HDR Imaging.Remote Sensing, 16(3):437, 2024

    2024

  15. [23]

    A 128×128 120 dB 15µs Latency Asynchronous Temporal Contrast Vision Sensor.IEEE Journal of Solid-State Circuits, 43(2):566–576, 2008

    Patrick Lichtsteiner, Christoph Posch, and Tobi Delbruck. A 128×128 120 dB 15µs Latency Asynchronous Temporal Contrast Vision Sensor.IEEE Journal of Solid-State Circuits, 43(2):566–576, 2008

    2008

  16. [24]

    Energy efficiency analysis of Spiking Neural Networks for space applications.Astrodynamics, 9(6):909–932, 2025

    Paolo Lunghi, Stefano Silvestrini, Dominik Dold, Gabriele Meoni, Alexander Had- jiivanov, and Dario Izzo. Energy efficiency analysis of Spiking Neural Networks for space applications.Astrodynamics, 9(6):909–932, 2025

    2025

  17. [25]

    McHarg, Imogen R

    Matthew G. McHarg, Imogen R. Jones, Zachary Wilcox, Richard L. Balthazor, Alexandre Marcireau, and Gregory Cohen. Falcon Neuro space-based observations of lightning using event-based sensors.Frontiers in Remote Sensing, 5, 2024

    2024

  18. [26]

    Data- Driven Feature Tracking for Event Cameras

    Nico Messikommer, Carter Fang, Mathias Gehrig, and Davide Scaramuzza. Data- Driven Feature Tracking for Event Cameras. InIEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2023

    2023

  19. [27]

    Event-based Vision Sensor Physics-Based Digital Twin for Tuning SSA Use

    Masashi Nishiguchi, Carolin Frueh, and Brian McReynolds. Event-based Vision Sensor Physics-Based Digital Twin for Tuning SSA Use. InAdvanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2024

    2024

  20. [28]

    Page, Joanne E

    Matthew J. Page, Joanne E. McKenzie, Patrick M. Bossuyt, Isabelle Boutron, Tammy C. Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer M. Tetzlaff, Elie A. Akl, Sue E. Brennan, Roger Chou, Julie Glanville, Jeremy M. Grimshaw, Asbjørn Hr´ objartsson, Manoj M. Lalu, Tianjin...

    2020

  21. [29]

    EMVS: Event-based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time

    Henri Rebecq, Guillermo Gallego, Elias Mueggler, and Davide Scaramuzza. EMVS: Event-based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time. InEuropean Conference on Computer Vision (ECCV), 2018

    2018

  22. [30]

    High Speed and High Dynamic Range Video with an Event Camera.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6):1964–1980, 2021

    Henri Rebecq, Rene Ranftl, Vladlen Koltun, and Davide Scaramuzza. High Speed and High Dynamic Range Video with an Event Camera.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6):1964–1980, 2021

    1964

  23. [31]

    Towards spike-based machine intelligence with neuromorphic computing.Nature, 575(7784):607–617, 2019

    Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. Towards spike-based machine intelligence with neuromorphic computing.Nature, 575(7784):607–617, 2019

    2019

  24. [32]

    Mohammed Salah, Mohammed Chehadah, Muhammad Humais, Mohammed Wah- bah, Abdulla Ayyad, Rana Azzam, Lakmal Seneviratne, and Yahya Zweiri. A Neu- romorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions.IEEE Transactions on Instrume...

    2024

  25. [33]

    SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks

    Xinyu Shi, Zecheng Hao, and Zhaofei Yu. SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  26. [34]

    Sparse- Gated RGB-Event Fusion for Small Object Detection in the Wild.Remote Sensing, 17(17):3112, 2025

    Yangsi Shi, Miao Li, Nuo Chen, Yihang Luo, Shiman He, and Wei An. Sparse- Gated RGB-Event Fusion for Small Object Detection in the Wild.Remote Sensing, 17(17):3112, 2025

    2025

  27. [35]

    Secrets of Event-Based Optical Flow

    Shintaro Shiba, Yoshimitsu Aoki, and Guillermo Gallego. Secrets of Event-Based Optical Flow. InEuropean Conference on Computer Vision (ECCV), 2022

    2022

  28. [36]

    Training-free AI for earth ob- servation change detection using physics aware neuromorphic networks.Scientific Reports, 15(1), 2025

    Stephen Smith, Cormac Purcell, and Zdenka Kuncic. Training-free AI for earth ob- servation change detection using physics aware neuromorphic networks.Scientific Reports, 15(1), 2025. 10 L. Capogrosso et al

    2025

  29. [37]

    Development of neuromorphic event-based imaging spectroscopy for hypersonic flight observa- tion.Aerospace Science and Technology, 168:111160, 2026

    Tamara Sopek, Fabian Zander, Byrenn Birch, and David Buttsworth. Development of neuromorphic event-based imaging spectroscopy for hypersonic flight observa- tion.Aerospace Science and Technology, 168:111160, 2026

    2026

  30. [38]

    Neuromorphic AI Onboard, 2024

    The European Space Agency (ESA). Neuromorphic AI Onboard, 2024. Accessed: 2026-03-24

    2024

  31. [39]

    David, Kevin M

    Manu Tom, C´ edric H. David, Kevin M. Marlis, Paul A. Zimdars, Quentin Bonassies, Jeffrey Wade, Arnaud Cerbelaud, Matthew Bonnema, Tamlin Pavel- sky, and Thomas Huang. Event-based serverless environmental modeling on the cloud: A pedagogical guide and river case study.Environm...

    2026

  32. [40]

    Remote sensing platforms and sensors: A survey.ISPRS Journal of Photogrammetry and Remote Sensing, 115:22–36, 2016

    Charles Toth and Grzegorz J´ o´ zk´ ow. Remote sensing platforms and sensors: A survey.ISPRS Journal of Photogrammetry and Remote Sensing, 115:22–36, 2016

    2016

  33. [41]

    van Rijn, Holger H

    Devis Tuia, Konrad Schindler, Beg¨ um Demir, Xiao Xiang Zhu, Mrinalini Kochupil- lai, Saˇ so Dˇ zeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Volker Markl, Bertrand Le Saux, Rochelle Schneider, and Gustau Camps-Valls. Artificial Intelligence to Advanc...

    2025

  34. [42]

    Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios

    Antoni Rosinol Vidal, Henri Rebecq, Timo Horstschaefer, and Davide Scaramuzza. Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios. InIEEE Robotics and Automation Letters (RA-L) with presentation at ICRA, 2018

    2018

  35. [43]

    STP-H12-VANTAGE: Combining Visual and Event-Based Sensing for Earth Observation, 2025

    Diego Wildenstein, Michael J Cannizzaro, Ian Peitzsch, Linus Silbernagel, Joshua Poravanthattil, Peter Drum, Mark Hofmeister, Cole Bowman, Natan Herzog, Evan W Gretok, et al. STP-H12-VANTAGE: Combining Visual and Event-Based Sensing for Earth Observation, 2025

    2025

  36. [44]

    Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks

    Yi Xiao, Qiangqiang Yuan, Kui Jiang, Wenke Huang, Qiang Zhang, Tingting Zheng, Chia-Wen Lin, and Liangpei Zhang. Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks. InConference on Neural Information Processing Syste...

    2025

  37. [45]

    A 10 000-Inference/s Bio-Inspired Spiking Vision Chip Based on an End-to-End SNN Embedding Image Signal Enhancement.IEEE Journal of Solid-State Circuits, 61(3):1164–1180, 2026

    Xu Yang, Fuming Lei, Na Tian, Cong Shi, Zhe Wang, Shuangming Yu, Runjiang Dou, Peng Feng, Nan Qi, Zhongming Wei, Jian Liu, Kaiyou Wang, Nanjian Wu, and Liyuan Liu. A 10 000-Inference/s Bio-Inspired Spiking Vision Chip Based on an End-to-End SNN Embedding Image Signal Enhanceme...

    2026

  38. [46]

    A Bio-Inspired Spiking Vision Chip Based on SPAD Imaging and Direct Spike Computing for Versatile Edge Vision.IEEE Journal of Solid-State Circuits, 59(6):1883–1898, 2024

    Xu Yang, Chunhe Yao, Lei Kang, Qian Luo, Nan Qi, Runjiang Dou, Shuangming Yu, Peng Feng, Zhongming Wei, Jian Liu, Kaiyou Wang, Nanjian Wu, and Liyuan Liu. A Bio-Inspired Spiking Vision Chip Based on SPAD Imaging and Direct Spike Computing for Versatile Edge Vision.IEEE Journal...

    2024

  39. [47]

    Wei Yao, Xin Shen, Guo Zhang, Zezhong Lu, Jiaying Wang, Yanjie Song, and Zhiwei Li. A spiking neural network based proximal policy optimization method for multi-point imaging mission scheduling of earth observation satellite.Swarm and Evolutionary Computation, 94:101867, 2025

    2025

  40. [48]

    Full-dof egomotion esti- mation for event cameras using geometric solvers

    Ji Zhao, Banglei Guan, Zibin Liu, and Laurent Kneip. Full-dof egomotion esti- mation for event cameras using geometric solvers. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

    2025

  41. [49]

    Semi-Dense 3D Reconstruction with a Stereo Event Camera

    Yi Zhou, Guillermo Gallego, Henri Rebecq, Laurent Kneip, Hongdong Li, and Davide Scaramuzza. Semi-Dense 3D Reconstruction with a Stereo Event Camera. InEuropean Conference on Computer Vision (ECCV), 2018

    2018

Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.