Point cloud data present a broad swath of intriguing problems in signal processing. Namely, the data may be sparse, may be non-uniformly sampled in space and time, and cannot be processed directly by way of conventional techniques such as convolutional filters. This paper addresses such data under the application umbrella of remote sensing. Specifically, we examine the potential of interferometric synthetic aperture radar for detecting geohazards that affect transportation. Using sparsely distributed coherent scatterers on the ground, our algorithms attempt to locate events in process such as sinkholes in the vicinity of highways. Theoretically, the problem boils down to the detection of Gaussian-shaped changes that evolve predictably in space and time. The solution to the detection problem involves two basic approaches, one grounded in pattern matching and the other in statistical signal processing. Essentially, the spatiotemporal pattern matching extends a Hough-like voting algorithm to a method that penalizes deviation from the known model in space and time. For confirmation of geohazard location, we can exploit a fixed-time analysis of the distribution of subsidence from the point cloud data by way of computing mutual information. Results show that the detection and screening strategies conform to geological evidence.