Semantic Segmentation in Geospatial Computer Vision

地理空间计算机视觉中的语义分割

• 批准号: RGPIN-2021-03479
• 负责人: Poullis, Charalambos
• 金额: $ 2.11万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750121
• 关键词: Semantic; Segmentation; Geospatial; Computer; Vision

Recently there have been tremendous advances in computer vision due to the advent of deep learning. Human-like performance has already been achieved for cognitive tasks involving visual and spatial processing. However, this high performance is strongly dependent on the number of training examples in the dataset. To address this, many online competitions are offering large benchmark datasets for training. The creation of training datasets of images or video is a costly and labour-intensive process. It requires a significant number of people to label the data and ensure its correctness and completeness manually. This is exacerbated by the fact that a dataset can typically only be used for a single classification task. This is especially the case in the geospatial domain working with remote sensor data where most datasets are for building/non-building classification. Despite the difficulties involved in the creation, researchers rely on large datasets for training classifiers to assist in solving more difficult problems such as reconstruction. Reconstructing large-scale urban areas is an inherently complex problem that involves several vision tasks. The first step is semantic segmentation(*), where the objective is to label each pixel into an urban feature type, e.g., building, road, tree, vegetation, cars, clutter etc. Next, the pixels are clustered based on their labels into contiguous groups corresponding to instances of the urban features they represent. Finally, the reconstruction is performed on each cluster, where a customized algorithm is applied according to the urban feature type corresponding to the cluster. Hence, as it is evident, to achieve a complete urban-area reconstruction, one must first address the problems relating to semantic segmentation(*). This research program builds upon our most recent research outcomes in creating large-scale realistic virtual environments and focuses on addressing some of the significant challenges identified so far. Specifically, this DG will investigate the following two research objectives: 1.Few-shot semantic segmentation. The objective is to investigate network architectures and training paradigms which enable network training using only a minimal set of training examples. 2.Interpretability. The objective is to investigate methods for analyzing and interpreting what the network is learning internally with the goal of re-purposing the abundant pre-trained semantic segmentation networks on auxiliary tasks relating to their primary task, without further training or fine-tuning. This research program is expected to make substantial contributions to the solution of complex problems of high practical relevance to the field of computer vision. (*)Semantic segmentation: each *pixel* has its class label; Classification: the *image* has a single class label. Acronyms used to describe the progress of PhD students/candidates: CE: Comprehensive Exam RP: Research Proposal DS: Doctoral Seminar

最近，由于深度学习的发展，计算机视觉的进步已取得了巨大进步。与视觉和空间处理有关的认知任务已经实现了类似人类的表现。但是，这种高性能在很大程度上取决于数据集中的培训示例数量。为了解决这个问题，许多在线竞赛提供了大型基准数据集用于培训。创建图像或视频的培训数据集是一个昂贵且劳动密集型的过程。需要大量的人标记数据并确保其正确性和完整性手动。由于数据集通常仅可用于单个分类任务，这使情况加剧了这一事实。在与遥控传感器数据一起工作的地理空间域中尤其如此，大多数数据集用于构建/非构建分类。尽管创作涉及困难，但研究人员依靠大型数据集来培训分类器来帮助解决更困难的问题，例如重建。重建大规模的城市地区是一个固有的复杂问题，涉及多个视觉任务。第一步是语义分割（*），其目的是将每个像素标记为城市特征类型，例如建筑物，道路，树木，树木，植被，汽车，混乱等。接下来，将像素基于标签聚集到与他们所代表的城市特征实例相对应的连续群体。最后，重建是在每个群集上执行的，其中根据与群集相对应的Urban特征类型应用自定义的算法。因此，正如证据的那样，要实现完整的城市地区重建，必须首先解决与语义细分有关的问题（*）。该研究计划基于我们在创建大规模逼真的虚拟环境方面的最新研究成果，并着重于解决迄今为止确定的一些重大挑战。特别是，该DG将研究以下两个研究目标：1。弹性语义细分。目的是研究网络体系结构和培训范例，该范例仅使用一组最小的培训示例来实现网络培训。 2.解释性。目的是研究用于分析和解释网络在内部学习的方法，目的是在不进行进一步的培训或微调的情况下重新填充与其主要任务有关的辅助任务，以重新定位有关其主要任务的辅助任务。预计该研究计划将为解决与计算机视野领域的高实践相关性的复杂问题做出重大贡献。 （*）语义细分：每个*像素*具有其类标签；分类： *图像 *具有单个类标签。用于描述博士学位学生/候选人的进度的首字母缩写词：CE：综合考试RP：研究建议DS：博士研讨会