EAGER: IMPRESS-U: Exploratory Research on Generative Compression for Compressive Lidar

EAGER：IMPRESS-U：压缩激光雷达生成压缩的探索性研究

• 批准号: 2404740
• 负责人: Gonzalo Arce
• 金额: $ 30万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2404740&HistoricalAwards=false
• 关键词: EAGER; IMPRESS; Exploratory; Research; Generative

This IMPRESS-U project is jointly funded by NSF, National Science Center of Poland, US National Academy of Sciences, and Office of Naval Research Global (DoD). The research will be performed in a multilateral international partnership that unites the University of Delaware, US, the National Aerospace University in Kharkiv, Ukraine, and the West Pomeranian University of Technology in Szczecin, Poland. US portion of the collaborative effort will be co-funded by Office of International Science and Engineering (OISE), Established Program to Stimulate Competitive Research (EPSCoR), and Communications and Information Foundations Program (CCF). The proposed effort aims at: (a) establishing a partnership among academic research teams from Ukraine, Poland, and the US; (b) building a resilient and collaborative research and education program of excellence in Ukraine in machine learning (ML) for satellite lidar sensing of Earth; and (c) exploring radically new concepts in generative compression for the storage or communication of compressive lidar measurements. The methods will be applied to data obtained from a new generation of satellite lidars, coined compressive lidars (CS lidar), that will be used to unravel the topological structure of the Earth’s surface and its forests, which have a profound effect on ecosystem processes. Spaceborne lidars today are limited in spatial resolution and coverage since laser reflections are only measured along 1D footprint line scans. Compressive lidars take sparse measurements of Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage, as if the data was collected from just hundreds of meters in height. To date, satellite lidars do not use data compression to avoid loss of information. CS lidars, however, rely on deep learning reconstruction algorithms covering orders of magnitude larger areas of Earth where the opportunity of data compression arises naturally. This project will thus explore radically new approaches to data compression using generative models which have shown the potential to produce more accurate image reconstructions at much deeper compression levels. The project will recruit a fresh cohort of talent in Ukraine and Poland who will be galvanized to embark on enduring careers that revolve around the intersection of machine learning and Earth remote sensing. Spaceborne lidar is an important imaging technology that is used to unravel the topological structure of the Earth’s surface and its forests which have a profound effect on ecosystem processes that determine nutrient, water, and carbon cycles on Earth. Spaceborne lidars today are limited in spatial resolution and coverage since laser reflections are only measured along 1D footprint line scans. In between these, vast amounts of landscape remain without sampling illumination. To overcome this limitation, a new generation of satellite lidars are being developed at NASA taking on a radically new sensing paradigm where the traditional 1D line scanning is abandoned and replaced by sparse and wide-field-of view lidar illumination, coined compressive satellite (CS) lidars. The objective is to compressively sense Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage, as if the data was collected from just hundreds of meters in height. NASA’s satellite lidars to date do not use data compression of the measurements to avoid loss of information. CS lidar, however, relies on deep learning reconstruction algorithms covering orders of magnitude larger areas of Earth where the opportunity of data compression arises naturally. NASA’s CS lidar team is currently not exploring this problem and thus the proposed exploratory research effort is valuable, complementary, and timely. While traditional image compression relies on hand-crafted encoder/decoder pairs, the research team will explore radically new approaches to data compression using generative models which have shown the potential to produce more accurate image reconstructions at much deeper compression levels. CS lidars promise to significantly enhance both the field-of-view and imaging resolution of satellite altimetry where data compression becomes increasingly important.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个印象深刻的项目由NSF，波兰国家科学中心，美国国家科学院和海军研究办公室（DOD）共同资助。这项研究将在多边国际合作伙伴关系中进行，该合作伙伴关系是特拉华大学，美国，乌克兰的哈尔基夫国家航空大学和波兰SZCZECIN的西波梅拉尼亚大学的国家航空航天大学。美国协作工作的美国部分将由国际科学与工程办公室（OISE）共同资助，刺激竞争研究（EPSCOR）的建立计划以及通信和信息基础计划（CCF）。拟议的努力旨在：（a）在乌克兰，波兰和美国的学术研究团队之间建立伙伴关系； （b）为卫星激光雷达的敏感性建立乌克兰机器学习（ML）的卓越卓越研究与教育计划； （c）探索一般压缩中的根本新概念，以存储或通信压缩激光雷达测量值。这些方法将应用于从新一代的卫星激光雷达（CS LIDAR）获得的数据，这些数据将用于揭示地球表面及其森林的拓扑结构，这对生态系统过程产生了深远的影响。当今的Spaceborne激光痛的空间分辨率和覆盖范围有限，因为激光反射仅沿1D足迹线扫描测量。压缩倍增倍数从地球上方数百公里的地球进行了稀疏的测量，然后以分辨率和覆盖范围重建了3D图像，就好像是从高度仅数百米处收集的数据一样。迄今为止，卫星激光雷达（Satellite LiDars）不使用数据压缩来避免信息丢失。但是，CS激光雷达依赖于深度学习重建算法，这些算法涵盖了地球较大区域的较大区域，在这些算法中，数据压缩的机会自然出现。因此，该项目将使用通用模型探索从根本上进行数据压缩的方法，这些模型表明有潜力在更深的压缩水平下产生更准确的图像重建。该项目将在乌克兰和波兰招募新的人才队伍，这些人才将被宣告促进持久的职业，这些职业围绕机器学习与地球遥远敏感性的交汇处。 Spaceborne Lidar是一种重要的成像技术，用于揭示地球表面及其森林的拓扑结构，对确定地球上营养，水和碳周期的生态系统过程产生深远影响。当今的Spaceborne激光痛的空间分辨率和覆盖范围有限，因为激光反射仅沿1D足迹线扫描测量。在这之间，大量的景观保留在没有取样照明的情况下。为了克服这一局限性，在NASA开发了新一代的卫星lider，采用了一种截然不同的新灵敏度范式，在该范式上，传统的1D线扫描被稀疏而广阔的视野照明取代，并取代了压缩的卫星（CS）lider。目的是从地球上方数百公里的数百公里以压缩地进行地球，然后以分辨率和覆盖范围重建3D图像，就好像数据是从高度仅数百米处收集的一样。 NASA迄今为止的卫星雷达（NASA）不使用测量值的数据压缩来避免信息丢失。但是，CS激光雷达依赖于深度学习重建算法，这些算法涵盖了地球上较大的地球区域，在这些算法中，数据压缩的机会自然出现。 NASA的CS LIDAR团队目前没有探索这个问题，因此拟议的探索性研究工作是有价值，完整和及时的。尽管传统的图像压缩依赖于手工制作的编码器/解码器对，但研究团队将使用通用模型来探索具有根本新的数据压缩方法，这些模型表明有潜力在更深的压缩水平下产生更准确的图像重建。 CS激光雷达（CS LiDars）承诺将显着增强卫星高度测定的视野和成像分辨率，在该卫星高度计的越来越重要。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。