BigPlantSens - Assessing the Synergies of Big Data and Deep Learning for the Remote Sensing of Plant Species

各种任务--包括研究、自然保护和林业、农业或生态系统服务评估等经济活动--都需要关于植物物种地理分布的准确信息。由于新的非常高的空间分辨率的卫星任务和无人驾驶航空器（UAV），有越来越多的地球观测数据的可用性揭示了高的空间和时间细节的植被模式。在植被遥感领域，卷积神经网络（CNN）等深度学习方法目前正在彻底改变模式和对象识别的可能性。因此，预计随着高分辨率传感器技术的进步，CNN将扩大我们确定空间清晰植被模式的能力。然而，CNN通常需要大量的参考观察来学习关键的图像特征。大数据方法可以提供训练CNN模型所需的这些参考观测。各种倡议（例如，CLEF; GBIF，Pl@ntNet）提供关于植物物种的大量标记图像数据，即，照片和物种名称。由于在开放数据领域的不断努力，这些图像数据集可以免费获取，并继续增长。然而，目前尚不清楚使用此类图像数据集训练的CNN模型是否直接适用于极高分辨率的地球观测数据，包括空间分辨率，质量和查看几何形状。因此，在拟议的项目中，我们的目标是评估大数据与高空间分辨率地球观测数据在全自动植被制图方面的协同作用。所提出的方法使用大数据，即标记有物种名称的免费图像来训练CNN模型。然后将训练好的模型应用于高分辨率地球观测数据，以揭示目标物种的空间分布。因此，我们试图识别用于训练的图像的哪些特征影响映射准确性（例如，采集几何结构、图像质量），并且我们将在训练之前根据这些特征开发用于过滤图像数据集的算法。具体来说，我们的研究问题是：1）如何准确地识别不同植物物种的空间分布，使用所提出的大数据方法结合深度学习和超高分辨率遥感数据？2)哪些关键因素决定了基于大数据的图像数据集对于CNN训练的价值，这些数据集可以使用深度学习进行有效过滤？3)地球观测数据的空间分辨率如何限制植物物种识别？

Various tasks - including research, nature conservation, and economic activities such as forestry, agriculture, or ecosystem service assessments - require accurate information on the geographical distribution of plant species. Due to novel very high spatial resolution satellite missions and Unmanned Aerial Vehicles (UAV), there is a growing availability of Earth observation data revealing both high spatial and temporal detail on vegetation patterns. Consequently, efficient methods are needed to harness this growing source of information for vegetation analysis.In the field of remote sensing of vegetation, Deep Learning methods such as Convolutional Neural Networks (CNN) are currently revolutionizing possibilities for pattern and object recognition. Thus, it is expected that, in tandem with advances in high-resolution sensor technology, CNN will enlarge our capability to determine spatially explicit vegetation patterns. However, CNN commonly require ample reference observations to learn the pivotal image features. A big data approach may provide these reference observations required for training the CNN models. Various initiatives (e.g., CLEF; GBIF, Pl@ntNet) provide a vast amount of labelled image data on plant species, i.e., photographs together with species names. As a result of the constant efforts in the area of Open Data, such image datasets are freely accessible and continue to grow. However, it remains unclear if CNN models trained with such image datasets are directly applicable to very-high-resolution Earth observation data in terms of their spatial resolution, quality, and viewing geometries. Accordingly, in the proposed project, we aim to assess the synergies of big data with high spatial resolution Earth observation data for fully automated vegetation mapping. The proposed approach uses big data in terms of freely available imagery tagged with species names to train CNN models. The trained models are then applied to high-resolution Earth observation data to reveal the spatial distribution of the target species. Thereby, we seek to identify which characteristics of the images used for training affect mapping accuracy (e.g., acquisition geometry, image quality), and we will develop an algorithm for filtering the image datasets according to these characteristics before training. Specifically, our research questions are:1) How accurately can the spatial distribution of different plant species be identified using the proposed big data approach combined with deep learning and very-high-resolution remote sensing data?2) What are the critical factors determining the value of Big Data-based image datasets for CNN training, and can these be efficiently filtered using deep learning?3) How does the spatial resolution of the Earth observation data limit the plant species identification?