Full list of authors: Vaduvescu, O.; Gorgan, D.; Copandean, D.; Bacu, V.; Stefanut, T.; Sabou, A.; Nandra, C.; Boldea, C.; Pinter, V.; Popescu, M.; Petrescu, E.; Bertesteanu, D.; Davison, T.; Pérez Toledo, F. M.; Boldea, A.; Predatu, M.; Zegmott, T.; Wilson, T. G.; Hudin, L.; Stanescu, M.; Stanica, A.; Buhulea, A.; Stoica, A.; Timpea, A.; Anghel, S.; Ciobanu, D.; Toma, R.; Casanova, V.; Stecklum, B.; Choque-Challapa, N.; Short, P.; Tudorica, A. In 2015 we started a PhD thesis aiming to write a moving objects processing system (MOPS) aimed to detect near Earth asteroids (NEAs) in astronomical surveys planned within the EURONEAR project. Based on this MOPS experience, in 2017 we proposed the NEARBY project to the Romanian Space Agency, which awarded funding to the Technical University of Cluj-Napoca (UTCN) and the University of Craiovafor building a cloud-based online platform to reduce survey images, detect, validate and report in near real time asteroid detections and NEA candidates. The NEARBY platform was built and is available at UTCN since Feb 2018, being tested during 5 pilot surveys observed in 2017-2018 with the Isaac Newton Telescope in La Palma. Two NEAs were discovered in Nov 2018 (2018 VQ1 and 2018 VN3), being recovered and reported to MPC within 2 hours. Other 4 discovered NEAs were found from a few dozen possible NEA candidates promptly being followed, allowing us to discover 22 Hungarias and 7 Mars crossing asteroids using the NEARBY platform. Compared with other few available software, NEARBY could detect more asteroids (by 8-41%), but scores less than human detection (by about 10%). Using resulted data, the astrometric accurancy, photometric limits and an INT NEA survey case study are presented as guidelines for planning future surveys. © 2021. This research has been supported by ROSA (Romanian Space Agency) by the Contract CDI-STAR 192/2017, NEARBY - Visual Analysis of Multidimensional Astrophysics Data for Moving Objects Detection. With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. Peer reviewed