Incorporating Spatially-Explicit Uncertainty Metrics in Image-Derived Classification of Impervious Surfaces

人类建造的不透水表面积(ISA)是人类改变自然环境的一个重要指标。ISA包括人行道、停车场、车道和屋顶。在这个项目中，将改进目前最先进的方法，使用一种新的图像分析方法来检测不透水表面区域。典型的单线程多光谱分类将扩展为层次化的多进程上下文分类。这种特定于上下文的多进程方法使用各种有针对性的机器学习算法，同时将问题细分为可能更容易的子问题。这些算法是根据每个底层任务的复杂程度来选择的。因此，它们：i)不限于单一的机器学习方法(例如，决策树、神经网络)，以及ii)支持各种按需输入的组合。除了高分类精度外，这种分层方法还允许通过将最终产品与空间上明确的不确定性指标相关联来识别有问题的案例。这种方法并不局限于ISA检测，而是可以扩展到其他多光谱分类问题(如植被、土壤)，人为构建的不透水表面积降低或消除了下层土壤的吸水能力。它对环境和人类健康有重大影响，因此在土地利用决策中发挥着重要作用。例如，不透水表面极大地增加了与风暴和融雪事件相关的峰值流量，增加了暴雨水超过溪流渠道能力时下游洪水泛滥的可能性。这种不透水表面积检测模型将提供与高级不确定度指标相关的准确结果。这种新的机器学习方法将增强图像分类算法，从而促进科学和工程的发展。此外，独特的监测结果针对的是不一定熟悉图像分类及其局限性的科学家(如城市建模人员、水文学家、生物学家)。该项目将为分析和预测土地利用动态的跨学科框架提供支持。它还将加强地理知识和理论，以及在城市扩张、环境退化和可持续发展等社会问题上的应用。

Human-constructed impervious surface area (ISA) is an important indicator of human alterations to natural environments. ISA includes sidewalks, parking lots, driveways, and rooftops. In this project, the current state-of-the-art methods will be improved with a novel image analysis approach for detecting impervious surface areas. Typical single-thread multispectral classification will be extended into a hierarchical multi-process context-specific classification. This context-specific multi-process approach uses a variety of targeted machine learning algorithms while segmenting the problem into potentially easier sub-problems. These algorithms are selected based on the complexity of each underlying task. Therefore, they: i) are not restricted to a single machine learning method (e.g. decision trees, neural networks), and ii) support combination of various on-demand inputs. In addition to high classification accuracy, this hierarchical methodology allows identification of problematic cases through association of the end product with spatially-explicit uncertainty metrics. This approach is not constrained to ISA detection, but could be extended to other multi-spectral classification problems (e.g. vegetation, soil).Human-constructed impervious surface area reduces or eliminates the capacity of the underlying soil to absorb water. It has a significant impact on the environment and human health, therefore playing an important role in land use decisions. For example, impervious surfaces dramatically increase peak discharges associated with storm and snowmelt events, increasing the likelihood of downstream flooding as storm waters exceed stream channel capacities. This impervious surface area detection model will provide accurate results associated with advanced uncertainty metrics. The novel machine learning approach will enhance image classification algorithms, thus advancing science and engineering. Furthermore, the unique monitoring results are targeted for scientists not necessarily familiar with image classification and its limitations (e.g. urban modelers, hydrologists, biologists). This project will support an interdisciplinary framework to analyze and predict land use dynamics. It will also enhance geographical knowledge and theories, as well as applications to societal issues, such as urban sprawl, environmental degradation and sustainable development.