Jordan Milton Malof

Conference Proceedings 2020 IEEE Winter Conference on Applications of Computer Vision Wacv 2020 · March 1, 2020 Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite) imagery benchmarks. However, these benchmark datasets only capture a small ... Full text Cite

Benchmarking deep inverse models over time, and the neural-adjoint method

Conference Advances in Neural Information Processing Systems · January 1, 2020 We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements. Recently many new approaches based upon deep learning have arisen, ge ... Cite

Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: Preliminary results

Conference International Geoscience and Remote Sensing Symposium IGARSS · July 1, 2019 Segmentation convolutional neural networks (CNNs) are now popular for the semantic segmentation (i.e., dense pixel-wise labeling) of remote sensing imagery, such as color or hyperspectral satellite imagery. In recent years a large number of hand-labeled da ... Full text Cite

A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results

Conference International Geoscience and Remote Sensing Symposium IGARSS · July 1, 2019 Segmentation convolutional neural networks (SCNNs) are now popular for the semantic segmentation (i.e., dense pixel-wise labeling) of remote sensing imagery, such as color or hyperspectral satellite imagery. One desirable property of SCNNs when applied to ... Full text Cite

Large-scale semantic classification: Outcome of the first year of inria aerial image labeling benchmark

Conference International Geoscience and Remote Sensing Symposium IGARSS · October 31, 2018 Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcome ... Full text Cite

Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

Conference 2017 9th International Workshop on Advanced Ground Penetrating Radar Iwagpr 2017 Proceedings · July 28, 2017 Ground Penetrating Radar (GPR) is a remote sensing modality that has been researched extensively for buried threat detection. For this purpose, algorithms can be developed to automatically determine the presence of such threats. To train such algorithms, s ... Full text Cite

A queuing model for designing multi-modality buried target detection systems: Preliminary results

Conference Proceedings of SPIE the International Society for Optical Engineering · January 1, 2015 Many remote sensing modalities have been developed for buried target detection, each one offering its own relative advantages over the others. As a result there has been interest in combining several modalities into a single detection platform that benefit ... Full text Cite

Automatic solar photovoltaic panel detection in satellite imagery

Conference 2015 International Conference on Renewable Energy Research and Applications Icrera 2015 · January 1, 2015 The quantity of rooftop solar photovoltaic (PV) installations has grown rapidly in the US in recent years. There is a strong interest among decision makers in obtaining high quality information about rooftop PV, such as the locations, power capacity, and e ... Full text Cite