The field of environmental monitoring is rapidly advancing with the development of innovative methods for bioacoustic classification and remote sensing. Recent research has focused on creating efficient and lightweight models for classifying species from soundscape recordings, such as the use of spectrogram tokenization and Hopfield neural networks. These approaches have shown promising results in terms of accuracy and computational efficiency, making them suitable for large-scale deployments. Additionally, remote sensing techniques are being improved with the integration of multi-modal data and foundation models, enabling more accurate predictions of habitat shifts and species distribution. Noteworthy papers in this area include: Distilling Spectrograms into Tokens, which introduced a novel pipeline for fast and lightweight bioacoustic classification, and First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network, which proposed a rapid and transparent model for bioacoustic analysis. These advancements have significant implications for conservation efforts and environmental monitoring, enabling more effective tracking and prediction of species populations and habitats.