With the recent advances in wireless technologies, high frequency radio has become the primary medium for long-distance communication. Among various types of modulation in signal transmission, morse code stands out due to its simplicity and efficiency in information transmission while costing small bandwidth. In practice, however, it is extremely laborious to locate morse signals in wideband communication. It's a needle in a haystack, if morse code is sent in random carrier waves at random period. To avoid this, automatic morse signal detection has become a challenging task in wireless morse communications, and new solutions could be derived from the latest machine learning techniques. In this paper, we propose a deep learning framework, namely DeepMorse, to blindly detect morse signals in wideband spectrum data. In particular, we first develop a multi-signal sensing module to retrieve signal candidates from wideband spectrum without prior knowledge. Then, we construct a CNN-based module to extract informative features from the located candidates, in order to distinguish the morse signal from other types of modulation. To evaluate the proposed DeepMorse model, we set up a testbed utilizing commercialized long-distance wireless communication devices. The experimental results demonstrate that DeepMorse is able to effectively detect morse signals and outperform the state-of-the-art methods on four real-world datasets. INDEX TERMS Deep learning, blind signal detection, morse code, wideband wireless spectrum.

Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.

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Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.