This paper presents a fully automated system capable of processing country-scale archives of Very High Resolution (VHR) multi-view imagery. It performs image co-registration, stereo photogrammetry, surface modeling, and true-orthorectification, using novel techniques for detecting occlusions and shadows. The system is tested on regions up to 10,000 km2as part of the IARPA SMART program, https://www.iarpa.gov/research-programs/smart. Challenges exist in operating at this scale, including processing thousands of sparsely collected images in time, inconsistent spatial coverage, weather and seasonal conditions, and multi-sensor multi-resolution acquisition. We present several innovations to overcome these challenges and enable processing of large data sets spanning country-size areas while controlling computational costs.

LR parsing techniques have long been studied as being efficient and powerful methods for processing context-free languages. A linear-time algorithm for recognizing languages representable by LR(k) grammars has long been known. Recognizing substrings of a context-free language is at least as hard as recognizing full strings of the language, since the latter problem easily reduces to the former. In this article we present a linear-time algorithm for recognizing substrings of LR(k) languages, thus showing that the substring recognition problem for these languages is no harder than the full string recognition problem. An interesting data structure, the Forest-Structured Stack, allows the algorithm to track all possible parses of a substring without loosing the efficiency of the original LR parser. We present the algorithm, prove its correctness, analyze its complexity, and mention several applications that have been constructed.

LR parsing techniques have long been studied as efficient and powerful methods for processing context free languages. A linear time algorithm for recognizing languages representable by LR(k) grammars has long been known. Recognizing substrings of a context-free language is at least as hard as recognizing full strings of the language, as the latter problem easily reduces to the former. In this paper we present a linear time algorithm for recognizing substrings of LR(k) languages, thus showing that the substring recognition problem for these languages is no harder than the full string recognition problem. An interesting data structure, the Forest Structured Stack, allows the algorithm to track all possible parses of a substring without loosing the efficiency of the original LR parser. We present the algorithm, prove its correctness, analyze its complexity, and mention several applications that have been constructed.