This sounds a similar tool you may want to check out - Ecodash.ai

Odor based methods would be interesting. Provided they didn't need to be replenished too often.

We find varied stimulus prevents habituation.

Hi Ahmed - I'm involved in a couple different initiatives that are leveraging AI/ML to assist in monitoring and detecting potentially suspicious and illegal trade. Here are some papers that we've already published -- and we have several more in the works/under review currently. I can post them here in this thread once published: 

●Truszkowski, J. M., R. Maor, R. Bin Yousuf, S. Biswas, C. Chater, P. Gasson, S. McQueen, M. Norman, J. Saunders, J. Simeone, N. Ramakrishnan, A. Antonelli, and V. Deklerck. pre-print, forthcoming. A probabilistic approach to estimating timber harvest location. https://ecoevorxiv.org/repository/view/5059/

● Datta, D., N. Self, J. Simeone, A. Meadows, W. Outhwaite, N. Elmqvist, and N. Ramakrishnan. 2023. TimberSleuth: Visual Anomaly Detection with Human Feedback for Mitigating the Illegal Timber Trade. Information Visualization. https://doi.org/10.1177/14738716231157081

● Datta, D., S. Muthiah, J. Simeone, and A. Meadows. 2021. Scrutinizing Shipment Records to Thwart Illegal Timber Trade. Outlier Detection and Description Workshop, ACM SIGKDD 2021. https://oddworkshop.github.io/assets/papers/7.pdf

● Datta, D., M.R. Islam, N. Self, A. Meadows, J. Simeone, W. Outhwaite, C. Hin Keong, A. Smith, L. Walker, and N. Ramakrishnan. 2020. Detecting Suspicious Timber Trades. Proceedings from the Thirty-Second Conference on Innovative Applications of Artificial Intelligence (IAAI-20). NY, NY. https://doi.org/10.1609/aaai.v34i08.7032

● Mortier, Thomas, Jakub Truszkowski, Marigold Norman, Markus Boner, Bogdan Buliga, Caspar Chater, Henry Jennings, et al. “A Framework for Tracing Timber Following the Ukraine Invasion.” Nature Plants 10, no. 3 (March 2024): 390–401. https://doi.org/10.1038/s41477-024-01648-5.
(The researchers from World Forest ID who lead the above paper - together with Virginia Tech Computer Science, UW Remote Sensing and Isotope labs, and me are all now on a NSF grant to further this work together). 

Also, here are a couple of other recent relevant papers by other teams of researchers: 

● Gore, Meredith L., Emily Griffin, Bistra Dilkina, Aaron Ferber, Stanley E. Griffis, Burcu B. Keskin, and John Macdonald. “Advancing Interdisciplinary Science for Disrupting Wildlife Trafficking Networks.” Proceedings of the National Academy of Sciences 120, no. 10 (March 7, 2023): e2208268120. https://doi.org/10.1073/pnas.2208268120.

●Wing, Kate, and Benjamin Woodward. “Advancing Artificial Intelligence in Fisheries Requires Novel Cross-Sector Collaborations.” ICES Journal of Marine Science, August 28, 2024, fsae118. https://doi.org/10.1093/icesjms/fsae118.

Hi Karen. 

It's actually possible that it might not necessarily be the high heat that is causing problems with the batteries. Lithium should be quite durable for the temperatures experienced inside the enclosure. Instead, the problem could likely be condensation. In an enclosure, if the sun shines directly on a sealed enclosure, the enclosure can internally heat up, evaporating any moisture in it and raising the dew point. If the external temperature then cools, the internal enclosure air that comes into contact with the cooler walls of the enclosure can form condensation droplets. If you've ever left a glass jar in the sun, you'd see this phenomenon. 

The problem is that if any condensation gets on the internal camera circuit board, its possible that it can potentially conduct enough current to increase the discharge rate of the batteries or in some cases, cause the whole device to go down. We've seen this happen in some of the field devices we deploy and have since protected critical circuitry against condensation.   

To avoid this situation, I'd recommend trying to keep your camera traps out of direct sunlight and putting a lot of dessicant inside before sealing them. Not sure if this solves your problem, but hopefully it might help. 

Hi Ben!

Great initiative! 

A review of deep learning techniques for detecting animals in aerial and satellite images

https://www.sciencedirect.com/science/article/pii/S1569843224000864#b0475 

lists a number of data sets (incl. one published by you it seems...) 

Also, @dmorris keeps a list of Terrestrial wild animal images (aerial/drone):

https://lila.science/otherdatasets#images-terrestrial-animals-drone

Read "List of other conservation data sets - LILA BC" on LILA BC

List of other conservation data sets - LILA BC

LILA BC - List of other conservation-related data sets

LILA BC • 16 Mar 2017

which seem like it might be useful for you.

Hi @benweinstein !

1. Surely a general detector might be very useful for detecting objects in aerial imagery! Maybe something similar to what MegaDetector does in camera trap images, generally detecting person, animal, and vehicle (and thus also empty photos). This could greatly improve semi-automated procedures. It could also serve as a first step for context-specific detectors or classifiers to be developed on top of this general one.
2. There is also the WAID dataset that is readily available. Our research group could also provide images containing cows, sheep, and deer from South America.

Best