Large-scale study of glaciers improves our understanding of global glacier
change and is imperative for monitoring the ecological environment, preventing
disasters, and studying the effects of global climate change. Glaciers in the
Hindu Kush Himalaya (HKH) are particularly interesting as the HKH is one of the
world's most sensitive regions for climate change. In this work, we: (1)
propose a modified version of the U-Net for large-scale, spatially
non-overlapping, clean glacial ice, and debris-covered glacial ice
segmentation; (2) introduce a novel self-learning boundary-aware loss to
improve debris-covered glacial ice segmentation performance; and (3) propose a
feature-wise saliency score to understand the contribution of each feature in
the multispectral Landsat 7 imagery for glacier mapping. Our results show that
the debris-covered glacial ice segmentation model trained using self-learning
boundary-aware loss outperformed the model trained using dice loss.
Furthermore, we conclude that red, shortwave infrared, and near-infrared bands
have the highest contribution toward debris-covered glacial ice segmentation
from Landsat 7 images.

通过此项工作，在大规模、空间不重叠的冰川和覆盖垃圾的冰川冰的分割方面，我们提出了一种改进版的 U-Net，并引入了一种新的自学习边界感知损失来提高冰川分割性能；对于冰川的多光谱 Landsat 7 图像，我们还提出了一种特征显着性评分来理解每个特征在冰川映射中的贡献；经过实验证明，使用自学习边界感知损失训练的冰川模型胜过使用 Dice loss 训练的模型，并且红色、短波红外线和近红外带对于 Landsat 7 图像中的覆盖垃圾的冰川分割具有最高的贡献度。

边界感知 U-Net 用于冰川分割

Boundary Aware U-Net for Glacier Segmentation

Obtaining detailed and reliable data about local economic livelihoods in
developing countries is expensive, and data are consequently scarce. Previous
work has shown that it is possible to measure local-level economic livelihoods
using high-resolution satellite imagery. However, such imagery is relatively
expensive to acquire, often not updated frequently, and is mainly available for
recent years. We train CNN models on free and publicly available multispectral
daytime satellite images of the African continent from the Landsat 7 satellite,
which has collected imagery with global coverage for almost two decades. We
show that despite these images' lower resolution, we can achieve accuracies
that exceed previous benchmarks.

使用 Landsat 7 卫星采集的多光谱卫星图像，训练 CNN 模型可以准确地测量发展中国家地方经济生计，即使图像分辨率较低，仍能超越以往的基准结果。