We find that segmentation and object detection models Models on: (1) building detection, (2) generalization to unseen viewing anglesĪnd resolutions, and (3) sensitivity of building footprint extraction toĬhanges in resolution. We benchmark multiple leading segmentation and object detection Same geography and are annotated with 126,747 building footprint labels,Įnabling direct assessment of the impact of viewpoint perturbation on model Range of viewing angles (-32 to 54 degrees). MVOI comprises 27 unique looks from a broad Multi-View Overhead Imagery (MVOI) Dataset, an extension of the SpaceNet open To address this problem, we introduce the SpaceNet The impact of these perturbations for algorithmic detection and segmentation of Represents an important challenge to computer vision methods, as changing viewĪngle adds distortions, alters resolution, and changes lighting. Natural disasters where first looks are often over 40 degrees off-nadir. In real-world overhead imagery, particularly in dynamic scenarios such as Universally comprise a single view taken from directly overhead ("at nadir"),įailing to address one critical variable: look angle. Though new overhead imagery datasets are being developed, they almost Instance-to-instance heterogeneity of objects in overhead imagery calls forĪpproaches distinct from existing models designed for natural scene datasets. The variable density, random orientation, small size, and Detection and segmentation of objects in overheard imagery is a challenging
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