Marc Böhlen

Sai Krishna Tammali, Vinaya Kumar, Marc Böhlen

This paper describes how AI models can be augmented and adapted to interpret landscapes. We present the technical framework of a Sentinel-2 satellite asset interpretation pipeline that combines statistical operations, human judgment, and generative AI models to produce succinct commentaries on industrial mining sites across the planet. To this end we introduce a novel bespoke landscape descriptor, the Urban Dwelling and Mining Index, and discuss how this metric can improve the performance of a multimodal language model in assessing the spatial distribution of mining operations.

Marc Böhlen, Varun Chandola, Wawan Sujarwo et al.

Image classifiers work effectively when applied on structured images, yet they often fail when applied on images with very high visual complexity. This paper describes experiments applying state-of-the-art object classifiers toward a unique set of images in the wild with high visual complexity collected on the island of Bali. The text describes differences between actual images in the wild and images from Imagenet, and then discusses a novel approach combining informational cues particular to video with an ensemble of imperfect classifiers in order to improve classification results on video sourced images of plants in the wild.

Marc Böhlen

This text argues for the potential of machine learning infused classification systems as vectors for a technically-engaged and constructive technology critique. The text describes this potential with several experiments in image data creation and neural network based classification. The text considers varying aspects of slippage in classification and considers the potential for discovery - as opposed to disaster - stemming from machine learning systems when they fail to perform as anticipated.