High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others. Unfortunately, high-resolution imagery is both infrequently collected and expensive to purchase, making it hard to efficiently and effectively scale these downstream tasks over both time and space. We propose a new conditional pixel synthesis model that uses abundant, low-cost, low-resolution imagery to generate accurate high-resolution imagery at locations and times in which it is unavailable. We show that our model attains photo-realistic sample quality and outperforms competing baselines on a key downstream task -- object counting -- particularly in geographic locations where conditions on the ground are changing rapidly.
翻译:高分辨率卫星图像已被证明对范围广泛的任务很有用,其中包括测量全球人类人口、当地经济生计和生物多样性等。不幸的是,高分辨率图像收集不频繁,购买费用昂贵,难以在时间和空间上高效率和高效益地扩大这些下游任务的规模。我们提议了一个新的有条件的像素合成模型,利用大量、低成本、低分辨率图像,在无法获得图像的地点和时间生成准确的高分辨率图像。我们表明,我们的模型达到了摄影现实的样本质量,在关键的下游任务 -- -- 物体计数 -- -- 上比相竞基线要好,特别是在地面条件正在迅速变化的地理位置。