Automated Processing of Remote Sensing Satellite Imagery using Machine Learning

Sightline Innovation是加拿大历史最悠久的人工智能（AI）公司，其业务围绕为客户提供机器学习和计算智能策略，平台和框架。Sightline的人工智能企业基础设施提供了一个集成的实时 ** 分布式人工智能产品即服务，可以有效地根据任何客户的需求进行定制。目前，Sightline** 的客户对处理卫星图像有兴趣，包括地面沉降检测、预测性维护、民用基础设施风险评估、水文管理和精准农业。Sightline在这一领域的客户群归因于越来越多的廉价存储、更广泛的卫星数据访问、按需计算和最先进的解决方案。Sightline的解决方案管道 ** 使各种各样的用例能够作为服务来解决。**由于Sightline正积极与处于卫星图像 ** 处理开发各个阶段的多个客户合作，因此必须针对每个客户解决两个关键的客户特定技术步骤：1）为特定于客户端应用程序的监督机器学习构建适当的训练集，以及2）选择 ** 适当的神经网络分类器，该神经网络分类器平衡分类准确性和鲁棒性与 ** 客户端应用的计算时间/成本。文献综述表明，应用于这些用例的机器学习 ** 正在积极研究中，并且缺乏对 ** 可用方法的比较分析。拟议的研究项目将开发和实施一类机器学习算法，能够自动分析卫星图像，用于许多用例。经过测试的分类器将 ** 提供给Sightline的客户，使Sightline能够继续 ** 为客户驱动的问题提供技术先进的解决方案，从而发展业务。

Sightline Innovation, Canada's longest standing Artificial Intelligence (AI) company, has built their business**around providing machine learning and computational intelligence strategies, platforms, and frameworks to**their customers. Sightline's Enterprise Infrastructure for Artificial Intelligence provides an integrated real-time**distributed AI product-as-a-service that can effectively be customized to any client's needs. Currently, Sightline**clients are interested in processing satellite images for a number of use cases including land subsidence**detection, predictive maintenance, civil infrastructure risk assessment, hydrology management, and precision**agriculture. Sightline's client base in this area is attributed to growing access to inexpensive storage, wider**access to satellite data, on-demand computing, and state of the art solutions. Sightline's solution pipeline**enables the diverse array of use cases to be addressed as a service.**As Sightline is actively working with multiple customers at various stages of development for satellite image**processing, two key client-specific technical steps must be addressed on a per-client basis: 1) construction of an**appropriate training set for supervised machine learning specific to the client's application, and 2) selection of**an appropriate neural network classifier that balances classification accuracy and robustness against**computational time/cost for the client's application. Literature reviews have indicated that machine learning**applied to these use cases is under active research and that there is a lack of comparative analysis among**available methods. The proposed research project will develop and implement a class of machine learning**algorithms capable of automating analysis of satellite images for numerous use cases. The tested classifiers will**be made available to Sightline's clients, allowing Sightline to grow their business by enabling them to continue**delivering technologically advanced solutions to client-driven problems.