GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous field applications. In this work, we present a GNSS-free global localization solution that contains a method of registering imaging radar on the ground with overhead RGB imagery, with joint optimization of relative poses from odometry and global poses from our overhead registration. Previous works have used various combinations of ground sensors and overhead imagery, and different feature extraction and matching methods. These include various handcrafted and deep-learning-based methods for extracting features from overhead imagery. Our work presents insights on extracting essential features from RGB overhead images for effective global localization against overhead imagery using only ground radar and a single georeferenced initial guess. We motivate our method by evaluating it on datasets in diverse geographic conditions and robotic platforms, including on an Unmanned Surface Vessel (USV) as well as urban and suburban driving datasets.

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask. The authors examined the \textit{zero-shot} image segmentation accuracy of SAM on a large number of vision benchmark tasks and found that SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks. The impressive generalization of SAM for segmentation has major implications for vision researchers working on natural imagery. In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development. We examine SAM's performance on a set of diverse and widely studied benchmark tasks. We find that SAM does often generalize well to overhead imagery, although it fails in some cases due to the unique characteristics of overhead imagery and its common target objects. We report on these unique systematic failure cases for remote sensing imagery that may comprise useful future research for the community.

Every day, observation satellites capture terabytes of imagery of the Earth's surface that feed into a wide variety of civil and military applications. This stream of data has grown so large that only automated methods can feasibly analyze it. One critical component of remote sensing analysis is object detection: locating objects of interest on the Earth's surface in overhead imagery. Automated object detection algorithms have advanced by leaps and bounds over the last decade, but they still require vast amounts of labeled data for training, which is expensive and tedious to produce. Any technique that can reduce the resources needed to label objects in overhead imagery is therefore desirable. Most existing datasets for training overhead object detectors are labeled with horizontal bounding boxes [1][2][3][4][5], object-aligned bounding boxes [6][7][8][9][10], or segmentation masks [11][12].

Performing machine learning (ML) and analyzing geospatial data are both hard problems requiring a lot of domain expertise. These limitations have historically meant that one needs to be an expert in both to perform even the most basic analyses, making advances in AI for overhead imagery difficult to achieve. We at CosmiQ Works have asked ourselves: is there anything we can do to reduce this barrier to entry, making it easier to apply machine learning methods to overhead imagery data? Enter Solaris, a new Python library for ML analysis of geospatial data from CosmiQ Works. Solaris builds upon SpaceNet's previous tool suite, SpaceNetUtilities, along with several other CosmiQ projects like BASISS to provide an end-to-end pipeline for geospatial AI. Would you prefer a basic command line interface so you can run a pre-trained model without learning Python?

Many techniques, using statistics or artificial intelligence, exist that help classify and identify areas on satellite imagery. This includes land use characteristics such as urban spaces, agriculture lands, forests, etc. However, recreating a ground-level image and perspective using satellite imagery has only recently been developed and is now an active area of research. Such work has the potential to not only classify land more accurately but it can also provide a ground-level perspective that indicates how it differs or is like other similar classes. One pioneering technique developed in providing ground-level views from satellite images was developed by the University of California, Merced.