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

The recent exploitation of natural resources and associated waste water
injection in the subsurface have induced many small and moderate earthquakes in
the tectonically quiet Central United States. This increase in seismic activity
has produced an exponential growth of seismic data recording, which brings the
necessity for efficient algorithms to reliably detect earthquakes among this
large amount of noisy data. Most current earthquake detection methods are
designed for moderate and large events and, consequently, they tend to miss
many of the low-magnitude earthquake that are masked by the seismic noise.
Perol et. al (2018) has focused on the problem of earthquake detection by using
a deep-learning approach: the authors proposed a convolutional neural network
(ConvNetQuake) to detect and locate earthquake events from seismic records.
This reports aims at reproducing part of the methodology proposed by the
author, which is the implementation of a convolutional neural network for
classification of events (i.e., earthquake vs. noise) from seismic records.

使用卷积神经网络 (ConvNetQuake) 检测和定位地震事件，以解决小型和中等级地震活动中的低幅度震动噪声的问题。