As the global population continues to expand, the demand for natural
resources increases. Unfortunately, human activities account for 23% of
greenhouse gas emissions. On a positive note, remote sensing technologies have
emerged as a valuable tool in managing our environment. These technologies
allow us to monitor land use, plan urban areas, and drive advancements in areas
such as agriculture, climate change mitigation, disaster recovery, and
environmental monitoring. Recent advances in AI, computer vision, and earth
observation data have enabled unprecedented accuracy in land use mapping. By
using transfer learning and fine-tuning with RGB bands, we achieved an
impressive 99.19% accuracy in land use analysis. Such findings can be used to
inform conservation and urban planning policies.

全球人口不断增长，对自然资源的需求也在增加。遗憾的是，人类活动占到了 23% 的温室气体排放。幸运的是，遥感技术已经成为管理我们的环境的有价值的工具。这些技术使我们能够监测土地利用，规划城市区域，并推动农业、气候变化缓解、灾难恢复和环境监测等领域的进展。通过利用迁移学习和细调 RGB 波段，我们在土地利用分析方面取得了令人印象深刻的 99.19% 准确度。这样的研究结果可以用来制定保护和城市规划政策。

使用 EuroSAT 和迁移学习进行土地利用和土地覆盖（LULC）的映射

Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer  Learning

Land use mapping is a fundamental yet challenging task in geographic science.
In contrast to land cover mapping, it is generally not possible using overhead
imagery. The recent, explosive growth of online geo-referenced photo
collections suggests an alternate approach to geographic knowledge discovery.
In this work, we present a general framework that uses ground-level images from
Flickr for land use mapping. Our approach benefits from several novel aspects.
First, we address the nosiness of the online photo collections, such as
imprecise geolocation and uneven spatial distribution, by performing location
and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our
indoor/outdoor classifier achieves state-of-the-art performance on several
bench- mark datasets and approaches human-level accuracy. Second, we utilize
high-level semantic image features extracted using deep learning, specifically
convolutional neural net- works, which allow us to achieve upwards of 76%
accuracy on a challenging eight class land use mapping problem.

使用 Flickr 上的地面图像和深度学习方法进行土地利用地图绘制，并针对地理位置和室内 / 室外进行筛选和半监督数据增强，从而最终实现了 76% 的准确性。